Rozpocznij

Wyszukaj w skryptach "价格在30元内股票"

First 5-Minute ORB Levels with Hour Offset### Indicator Overview: First 5-Minute ORB Levels with Hour Offset This indicator is designed for traders who want to track the high and low of the first 5-minute candle of a trading session, specifically starting at 9:30 am EST (New York time) by default. The lines representing these levels, known as the "Opening Range Breakout" (ORB) levels, are extended across the trading session until the market close at 4:00 pm EST. The indicator provides the following features: 1. **Real-Time Updates**: - As the first 5-minute candle of the session forms (from 9:30 am to 9:35 am EST), the indicator dynamically updates the high and low lines. - After the candle completes, the lines are locked in place and extend horizontally across the chart until market close. 2. **Customizable Hour Offset**: - Users can adjust the start time of the session by specifying an hour offset. This feature is particularly useful for traders operating in different time zones or those who want to analyze custom session times. - For example, if you trade in a time zone where the session starts at 8:30 am local time instead of 9:30 am EST, you can set the hour offset to `-1` to adjust the start time accordingly. 3. **Visual Labels**: - The indicator places labels at the end of the lines, clearly marking the "5m ORB High" and "5m ORB Low" levels. These labels are updated in real-time as the first 5-minute candle forms and are fixed in place once the candle closes. ### How to Adjust the Settings: 1. **Hour Offset**: - **Description**: The hour offset allows you to shift the start time of the session. The default start time is 9:30 am EST, but you can change this using the hour offset. - **How to Adjust**: - Open the indicator settings. - Locate the "Hour Offset" field. - Enter a positive or negative integer value to shift the session start time. - **Example**: - `0` (default): Start at 9:30 am EST. - `-1`: Start at 8:30 am EST. - `+1`: Start at 10:30 am EST. - The indicator will then track the first 5-minute candle starting at the adjusted time and plot the high and low accordingly. 2. **Line and Label Appearance**: - The lines representing the ORB levels are green by default, and the labels are also green with white text for clear visibility on the chart. The labels are positioned to the right of the lines to avoid cluttering the chart. ### Use Cases: - **Opening Range Breakout Strategy**: Traders often use the ORB strategy to identify potential breakout points during the trading day. By marking the high and low of the first 5-minute candle, this indicator helps traders quickly identify key levels where price might break out or reverse. - **Custom Session Analysis**: If you trade in a different time zone or need to analyze a different session (e.g., pre-market or after-hours), the hour offset feature allows you to adapt the indicator to your needs. This indicator is particularly valuable for intraday traders who rely on the initial volatility of the trading session to make informed decisions.
Wskaźnik Pine Script®
od Brockjava
7
PubLibCandleTrendLibrary "PubLibCandleTrend" candle trend, multi-part candle trend, multi-part green/red candle trend, double candle trend and multi-part double candle trend conditions for indicator and strategy development chh()   candle higher high condition   Returns: bool chl()   candle higher low condition   Returns: bool clh()   candle lower high condition   Returns: bool cll()   candle lower low condition   Returns: bool cdt()   candle double top condition   Returns: bool cdb()   candle double bottom condition   Returns: bool gc()   green candle condition   Returns: bool gchh()   green candle higher high condition   Returns: bool gchl()   green candle higher low condition   Returns: bool gclh()   green candle lower high condition   Returns: bool gcll()   green candle lower low condition   Returns: bool gcdt()   green candle double top condition   Returns: bool gcdb()   green candle double bottom condition   Returns: bool rc()   red candle condition   Returns: bool rchh()   red candle higher high condition   Returns: bool rchl()   red candle higher low condition   Returns: bool rclh()   red candle lower high condition   Returns: bool rcll()   red candle lower low condition   Returns: bool rcdt()   red candle double top condition   Returns: bool rcdb()   red candle double bottom condition   Returns: bool chh_1p()   1-part candle higher high condition   Returns: bool chh_2p()   2-part candle higher high condition   Returns: bool chh_3p()   3-part candle higher high condition   Returns: bool chh_4p()   4-part candle higher high condition   Returns: bool chh_5p()   5-part candle higher high condition   Returns: bool chh_6p()   6-part candle higher high condition   Returns: bool chh_7p()   7-part candle higher high condition   Returns: bool chh_8p()   8-part candle higher high condition   Returns: bool chh_9p()   9-part candle higher high condition   Returns: bool chh_10p()   10-part candle higher high condition   Returns: bool chh_11p()   11-part candle higher high condition   Returns: bool chh_12p()   12-part candle higher high condition   Returns: bool chh_13p()   13-part candle higher high condition   Returns: bool chh_14p()   14-part candle higher high condition   Returns: bool chh_15p()   15-part candle higher high condition   Returns: bool chh_16p()   16-part candle higher high condition   Returns: bool chh_17p()   17-part candle higher high condition   Returns: bool chh_18p()   18-part candle higher high condition   Returns: bool chh_19p()   19-part candle higher high condition   Returns: bool chh_20p()   20-part candle higher high condition   Returns: bool chh_21p()   21-part candle higher high condition   Returns: bool chh_22p()   22-part candle higher high condition   Returns: bool chh_23p()   23-part candle higher high condition   Returns: bool chh_24p()   24-part candle higher high condition   Returns: bool chh_25p()   25-part candle higher high condition   Returns: bool chh_26p()   26-part candle higher high condition   Returns: bool chh_27p()   27-part candle higher high condition   Returns: bool chh_28p()   28-part candle higher high condition   Returns: bool chh_29p()   29-part candle higher high condition   Returns: bool chh_30p()   30-part candle higher high condition   Returns: bool chl_1p()   1-part candle higher low condition   Returns: bool chl_2p()   2-part candle higher low condition   Returns: bool chl_3p()   3-part candle higher low condition   Returns: bool chl_4p()   4-part candle higher low condition   Returns: bool chl_5p()   5-part candle higher low condition   Returns: bool chl_6p()   6-part candle higher low condition   Returns: bool chl_7p()   7-part candle higher low condition   Returns: bool chl_8p()   8-part candle higher low condition   Returns: bool chl_9p()   9-part candle higher low condition   Returns: bool chl_10p()   10-part candle higher low condition   Returns: bool chl_11p()   11-part candle higher low condition   Returns: bool chl_12p()   12-part candle higher low condition   Returns: bool chl_13p()   13-part candle higher low condition   Returns: bool chl_14p()   14-part candle higher low condition   Returns: bool chl_15p()   15-part candle higher low condition   Returns: bool chl_16p()   16-part candle higher low condition   Returns: bool chl_17p()   17-part candle higher low condition   Returns: bool chl_18p()   18-part candle higher low condition   Returns: bool chl_19p()   19-part candle higher low condition   Returns: bool chl_20p()   20-part candle higher low condition   Returns: bool chl_21p()   21-part candle higher low condition   Returns: bool chl_22p()   22-part candle higher low condition   Returns: bool chl_23p()   23-part candle higher low condition   Returns: bool chl_24p()   24-part candle higher low condition   Returns: bool chl_25p()   25-part candle higher low condition   Returns: bool chl_26p()   26-part candle higher low condition   Returns: bool chl_27p()   27-part candle higher low condition   Returns: bool chl_28p()   28-part candle higher low condition   Returns: bool chl_29p()   29-part candle higher low condition   Returns: bool chl_30p()   30-part candle higher low condition   Returns: bool clh_1p()   1-part candle lower high condition   Returns: bool clh_2p()   2-part candle lower high condition   Returns: bool clh_3p()   3-part candle lower high condition   Returns: bool clh_4p()   4-part candle lower high condition   Returns: bool clh_5p()   5-part candle lower high condition   Returns: bool clh_6p()   6-part candle lower high condition   Returns: bool clh_7p()   7-part candle lower high condition   Returns: bool clh_8p()   8-part candle lower high condition   Returns: bool clh_9p()   9-part candle lower high condition   Returns: bool clh_10p()   10-part candle lower high condition   Returns: bool clh_11p()   11-part candle lower high condition   Returns: bool clh_12p()   12-part candle lower high condition   Returns: bool clh_13p()   13-part candle lower high condition   Returns: bool clh_14p()   14-part candle lower high condition   Returns: bool clh_15p()   15-part candle lower high condition   Returns: bool clh_16p()   16-part candle lower high condition   Returns: bool clh_17p()   17-part candle lower high condition   Returns: bool clh_18p()   18-part candle lower high condition   Returns: bool clh_19p()   19-part candle lower high condition   Returns: bool clh_20p()   20-part candle lower high condition   Returns: bool clh_21p()   21-part candle lower high condition   Returns: bool clh_22p()   22-part candle lower high condition   Returns: bool clh_23p()   23-part candle lower high condition   Returns: bool clh_24p()   24-part candle lower high condition   Returns: bool clh_25p()   25-part candle lower high condition   Returns: bool clh_26p()   26-part candle lower high condition   Returns: bool clh_27p()   27-part candle lower high condition   Returns: bool clh_28p()   28-part candle lower high condition   Returns: bool clh_29p()   29-part candle lower high condition   Returns: bool clh_30p()   30-part candle lower high condition   Returns: bool cll_1p()   1-part candle lower low condition   Returns: bool cll_2p()   2-part candle lower low condition   Returns: bool cll_3p()   3-part candle lower low condition   Returns: bool cll_4p()   4-part candle lower low condition   Returns: bool cll_5p()   5-part candle lower low condition   Returns: bool cll_6p()   6-part candle lower low condition   Returns: bool cll_7p()   7-part candle lower low condition   Returns: bool cll_8p()   8-part candle lower low condition   Returns: bool cll_9p()   9-part candle lower low condition   Returns: bool cll_10p()   10-part candle lower low condition   Returns: bool cll_11p()   11-part candle lower low condition   Returns: bool cll_12p()   12-part candle lower low condition   Returns: bool cll_13p()   13-part candle lower low condition   Returns: bool cll_14p()   14-part candle lower low condition   Returns: bool cll_15p()   15-part candle lower low condition   Returns: bool cll_16p()   16-part candle lower low condition   Returns: bool cll_17p()   17-part candle lower low condition   Returns: bool cll_18p()   18-part candle lower low condition   Returns: bool cll_19p()   19-part candle lower low condition   Returns: bool cll_20p()   20-part candle lower low condition   Returns: bool cll_21p()   21-part candle lower low condition   Returns: bool cll_22p()   22-part candle lower low condition   Returns: bool cll_23p()   23-part candle lower low condition   Returns: bool cll_24p()   24-part candle lower low condition   Returns: bool cll_25p()   25-part candle lower low condition   Returns: bool cll_26p()   26-part candle lower low condition   Returns: bool cll_27p()   27-part candle lower low condition   Returns: bool cll_28p()   28-part candle lower low condition   Returns: bool cll_29p()   29-part candle lower low condition   Returns: bool cll_30p()   30-part candle lower low condition   Returns: bool gc_1p()   1-part green candle condition   Returns: bool gc_2p()   2-part green candle condition   Returns: bool gc_3p()   3-part green candle condition   Returns: bool gc_4p()   4-part green candle condition   Returns: bool gc_5p()   5-part green candle condition   Returns: bool gc_6p()   6-part green candle condition   Returns: bool gc_7p()   7-part green candle condition   Returns: bool gc_8p()   8-part green candle condition   Returns: bool gc_9p()   9-part green candle condition   Returns: bool gc_10p()   10-part green candle condition   Returns: bool gc_11p()   11-part green candle condition   Returns: bool gc_12p()   12-part green candle condition   Returns: bool gc_13p()   13-part green candle condition   Returns: bool gc_14p()   14-part green candle condition   Returns: bool gc_15p()   15-part green candle condition   Returns: bool gc_16p()   16-part green candle condition   Returns: bool gc_17p()   17-part green candle condition   Returns: bool gc_18p()   18-part green candle condition   Returns: bool gc_19p()   19-part green candle condition   Returns: bool gc_20p()   20-part green candle condition   Returns: bool gc_21p()   21-part green candle condition   Returns: bool gc_22p()   22-part green candle condition   Returns: bool gc_23p()   23-part green candle condition   Returns: bool gc_24p()   24-part green candle condition   Returns: bool gc_25p()   25-part green candle condition   Returns: bool gc_26p()   26-part green candle condition   Returns: bool gc_27p()   27-part green candle condition   Returns: bool gc_28p()   28-part green candle condition   Returns: bool gc_29p()   29-part green candle condition   Returns: bool gc_30p()   30-part green candle condition   Returns: bool rc_1p()   1-part red candle condition   Returns: bool rc_2p()   2-part red candle condition   Returns: bool rc_3p()   3-part red candle condition   Returns: bool rc_4p()   4-part red candle condition   Returns: bool rc_5p()   5-part red candle condition   Returns: bool rc_6p()   6-part red candle condition   Returns: bool rc_7p()   7-part red candle condition   Returns: bool rc_8p()   8-part red candle condition   Returns: bool rc_9p()   9-part red candle condition   Returns: bool rc_10p()   10-part red candle condition   Returns: bool rc_11p()   11-part red candle condition   Returns: bool rc_12p()   12-part red candle condition   Returns: bool rc_13p()   13-part red candle condition   Returns: bool rc_14p()   14-part red candle condition   Returns: bool rc_15p()   15-part red candle condition   Returns: bool rc_16p()   16-part red candle condition   Returns: bool rc_17p()   17-part red candle condition   Returns: bool rc_18p()   18-part red candle condition   Returns: bool rc_19p()   19-part red candle condition   Returns: bool rc_20p()   20-part red candle condition   Returns: bool rc_21p()   21-part red candle condition   Returns: bool rc_22p()   22-part red candle condition   Returns: bool rc_23p()   23-part red candle condition   Returns: bool rc_24p()   24-part red candle condition   Returns: bool rc_25p()   25-part red candle condition   Returns: bool rc_26p()   26-part red candle condition   Returns: bool rc_27p()   27-part red candle condition   Returns: bool rc_28p()   28-part red candle condition   Returns: bool rc_29p()   29-part red candle condition   Returns: bool rc_30p()   30-part red candle condition   Returns: bool cdut()   candle double uptrend condition   Returns: bool cddt()   candle double downtrend condition   Returns: bool cdut_1p()   1-part candle double uptrend condition   Returns: bool cdut_2p()   2-part candle double uptrend condition   Returns: bool cdut_3p()   3-part candle double uptrend condition   Returns: bool cdut_4p()   4-part candle double uptrend condition   Returns: bool cdut_5p()   5-part candle double uptrend condition   Returns: bool cdut_6p()   6-part candle double uptrend condition   Returns: bool cdut_7p()   7-part candle double uptrend condition   Returns: bool cdut_8p()   8-part candle double uptrend condition   Returns: bool cdut_9p()   9-part candle double uptrend condition   Returns: bool cdut_10p()   10-part candle double uptrend condition   Returns: bool cdut_11p()   11-part candle double uptrend condition   Returns: bool cdut_12p()   12-part candle double uptrend condition   Returns: bool cdut_13p()   13-part candle double uptrend condition   Returns: bool cdut_14p()   14-part candle double uptrend condition   Returns: bool cdut_15p()   15-part candle double uptrend condition   Returns: bool cdut_16p()   16-part candle double uptrend condition   Returns: bool cdut_17p()   17-part candle double uptrend condition   Returns: bool cdut_18p()   18-part candle double uptrend condition   Returns: bool cdut_19p()   19-part candle double uptrend condition   Returns: bool cdut_20p()   20-part candle double uptrend condition   Returns: bool cdut_21p()   21-part candle double uptrend condition   Returns: bool cdut_22p()   22-part candle double uptrend condition   Returns: bool cdut_23p()   23-part candle double uptrend condition   Returns: bool cdut_24p()   24-part candle double uptrend condition   Returns: bool cdut_25p()   25-part candle double uptrend condition   Returns: bool cdut_26p()   26-part candle double uptrend condition   Returns: bool cdut_27p()   27-part candle double uptrend condition   Returns: bool cdut_28p()   28-part candle double uptrend condition   Returns: bool cdut_29p()   29-part candle double uptrend condition   Returns: bool cdut_30p()   30-part candle double uptrend condition   Returns: bool cddt_1p()   1-part candle double downtrend condition   Returns: bool cddt_2p()   2-part candle double downtrend condition   Returns: bool cddt_3p()   3-part candle double downtrend condition   Returns: bool cddt_4p()   4-part candle double downtrend condition   Returns: bool cddt_5p()   5-part candle double downtrend condition   Returns: bool cddt_6p()   6-part candle double downtrend condition   Returns: bool cddt_7p()   7-part candle double downtrend condition   Returns: bool cddt_8p()   8-part candle double downtrend condition   Returns: bool cddt_9p()   9-part candle double downtrend condition   Returns: bool cddt_10p()   10-part candle double downtrend condition   Returns: bool cddt_11p()   11-part candle double downtrend condition   Returns: bool cddt_12p()   12-part candle double downtrend condition   Returns: bool cddt_13p()   13-part candle double downtrend condition   Returns: bool cddt_14p()   14-part candle double downtrend condition   Returns: bool cddt_15p()   15-part candle double downtrend condition   Returns: bool cddt_16p()   16-part candle double downtrend condition   Returns: bool cddt_17p()   17-part candle double downtrend condition   Returns: bool cddt_18p()   18-part candle double downtrend condition   Returns: bool cddt_19p()   19-part candle double downtrend condition   Returns: bool cddt_20p()   20-part candle double downtrend condition   Returns: bool cddt_21p()   21-part candle double downtrend condition   Returns: bool cddt_22p()   22-part candle double downtrend condition   Returns: bool cddt_23p()   23-part candle double downtrend condition   Returns: bool cddt_24p()   24-part candle double downtrend condition   Returns: bool cddt_25p()   25-part candle double downtrend condition   Returns: bool cddt_26p()   26-part candle double downtrend condition   Returns: bool cddt_27p()   27-part candle double downtrend condition   Returns: bool cddt_28p()   28-part candle double downtrend condition   Returns: bool cddt_29p()   29-part candle double downtrend condition   Returns: bool cddt_30p()   30-part candle double downtrend condition   Returns: bool
Biblioteka Pine Script®
od theEccentricTrader
2
Multi SMA EMA WMA HMA BB (5x8 MAs Bollinger Bands) MAX MTF - RRBMulti SMA EMA WMA HMA 4x7 Moving Averages with Bollinger Bands MAX MTF by RagingRocketBull 2019 Version 1.0 All available MAX MTF versions are listed below (They are very similar and I don't want to publish them as separate indicators): ver 1.0: 4x7 = 28 MTF MAs + 28 Levels + 3 BB = 59 < 64 ver 2.0: 5x6 = 30 MTF MAs + 30 Levels + 3 BB = 63 < 64 ver 3.0: 3x10 = 30 MTF MAs + 30 Levels + 3 BB = 63 < 64 ver 4.0: 5(4+1)x8 = 8 CurTF MAs + 32 MTF MAs + 20 Levels + 3 BB = 63 < 64 ver 5.0: 6(5+1)x6 = 6 CurTF MAs + 30 MTF MAs + 24 Levels + 3 BB = 63 < 64 ver 6.0: 4(3+1)x10 = 10 CurTF MAs + 30 MTF MAs + 20 Levels + 3 BB = 63 < 64 Fib numbers: 8, 13, 21, 34, 55, 89, 144, 233, 377 This indicator shows multiple MAs of any type SMA EMA WMA HMA etc with BB and MTF support, can show MAs as dynamically moving levels. There are 4 MA groups + 1 BB group, a total of 4 TFs * 7 MAs = 28 MAs. You can assign any type/timeframe combo to a group, for example: - EMAs 9,12,26,50,100,200,400 x H1, H4, D1, W1 (4 TFs x 7 MAs x 1 type) - EMAs 8,13,21,30,34,50,55,89,100,144,200,233,377,400 x M15, H1 (2 TFs x 14 MAs x 1 type) - D1 EMAs and SMAs 8,13,21,30,34,50,55,89,100,144,200,233,377,400 (1 TF x 14 MAs x 2 types) - H1 WMAs 13,21,34,55,89,144,233; H4 HMAs 9,12,26,50,100,200,400; D1 EMAs 12,26,89,144,169,233,377; W1 SMAs 9,12,26,50,100,200,400 (4 TFs x 7 MAs x 4 types) - +1 extra MA type/timeframe for BB There are several versions: Simple, MTF, Pro MTF, Advanced MTF, MAX MTF and Ultimate MTF. This is the MAX MTF version. The Differences are listed below. All versions have BB - Simple: you have 2 groups of MAs that can be assigned any type (5+5) - MTF: +2 custom Timeframes for each group (2x5 MTF) +1 TF for BB, TF XY smoothing - Pro MTF: 4 custom Timeframes for each group (4x3 MTF), 1 TF for BB, MA levels and show max bars back options - Advanced MTF: +4 extra MAs/group (4x7 MTF), custom Ticker/Symbols, Timeframe <>= filter, Remove Duplicates Option - MAX MTF: +2 subtypes/group, packed to the limit with max possible MAs/TFs: 4x7, 5x6, 3x10, 4(3+1)x10, 5(4+1)x8, 6(5+1)x6 - Ultimate MTF: +individual settings for each MA, custom Ticker/Symbols MAX MTF version tests the limits of Pinescript trying to squeeze as many MAs/TFs as possible into a single indicator. It's basically a maxed out Advanced version with subtypes allowing for mixed types within a group (i.e. both emas and smas in a single group/TF) Pinescript has the following limits: - max 40 security calls (6 calls are reserved for dupe checks and smoothing, 2 are used for BB, so only 32 calls are available) - max 64 plot outputs (BB uses 3 outputs, so only 61 plot outputs are available) - max 50000 (50kb) size of the compiled code Based on those limits, you can only have the following MAs/TFs combos in a single script: 1. 4x7, 5x6, 3x10 - total number of MTF MAs must always be <= 32, and you can still have BB and Num Levels = total MAs, without any compromises 2. 5(4+1)x8, 6(5+1)x6, 4(3+1)x10 - you can use the Current Symbol/Timeframe as an extra (+1) fixed TF with the same number of MTF MAs - you don't need to call security to display MAs on the Current Symbol/Timeframe, so the total number of MTF MAs remains the same and is still <= 32 - to fit that many MAs into the max 64 plot outputs limit you need to reduce the number of levels (not every MA Group will have corresponding levels) Features: - 4x7 = 28 MAs of any type - 4x MTF groups with XY step line smoothing - +1 extra TF/type for BB MAs - 2 MA subtypes within each group/TF - 4x7 = 28 MA levels with adjustable group offsets, indents and shift - supports any existing type of MA: SMA, EMA, WMA, Hull Moving Average (HMA) - custom tickers/symbols for each group - show max bars back option - show/hide both groups of MAs/levels/BB and individual MAs - timeframe filter: show only MAs/Levels with TFs <>= Current TF - hide MAs/Levels with duplicate TFs - support for custom TFs that are not available in free accounts: 2D, 3D etc - support for timeframes in H: H, 2H, 4H etc Notes: - Uses timeframe textbox instead of input resolution dropdown to allow for 240 120 and other custom TFs - Uses symbol textbox instead of input symbol to avoid establishing multiple dummy security connections to the current ticker - otherwise empty symbols will prevent script from running - Possible reasons for missing MAs on a chart: - there may not be enough bars in history to start plotting it. For example, W1 EMA200 needs at least 200 bars on a weekly chart. - for charts with low/fractional prices i.e. 0.00002 << 0.001 (default Y smoothing step) decrease Y smoothing as needed (set Y = 0.0000001) or disable it completely (set X,Y to 0,0) - for charts with high price values i.e. 20000 >> 0.001 increase Y smoothing as needed (set Y = 10-20). Higher values exceeding MAs point density will cause it to disappear as there will be no points to plot. Different TFs may require diff adjustments - TradingView Replay Mode UI and Pinescript security calls are limited to TFs >= D (D,2D,W,MN...) for free accounts - attempting to plot any TF < D1 in Replay Mode will only result in straight lines, but all TFs will work properly in history and real-time modes. This is not a bug. - Max Bars Back (num_bars) is limited to 5000 for free accounts (10000 for paid), will show error when exceeded. To plot on all available history set to 0 (default) - Slow load/redraw times. This indicator becomes slower, its UI less responsive when: - Pinescript Node.js graphics library is too slow and inefficient at plotting bars/objects in a browser window. Code optimization doesn't help much - the graphics engine is the main reason for general slowness. - the chart has a long history (10000+ bars) in a browser's cache (you have scrolled back a couple of screens in a max zoom mode). - Reload the page/Load a fresh chart and then apply the indicator or - Switch to another Timeframe (old TF history will still remain in cache and that TF will be slow) - in max possible zoom mode around 4500 bars can fit on 1 screen - this also slows down responsiveness. Reset Zoom level - initial load and redraw times after a param change in UI also depend on TF. For example: D1/W1 - 2 sec, H1/H4 - 5-6 sec, M30 - 10 sec, M15/M5 - 4 sec, M1 - 5 sec. M30 usually has the longest history (up to 16000 bars) and W1 - the shortest (1000 bars). - when indicator uses more MAs (plots) and timeframes it will redraw slower. Seems that up to 5 Timeframes is acceptable, but 6+ Timeframes can become very slow. - show_last=last_bars plot limit doesn't affect load/redraw times, so it was removed from MA plot - Max Bars Back (num_bars) default/custom set UI value doesn't seem to affect load/redraw times - In max zoom mode all dynamic levels disappear (they behave like text) - Dupe check includes symbol: symbol, tf, both subtypes - all must match for a duplicate group - For the dupe check to work correctly a custom symbol must always include an exchange prefix. BB is not checked for dupes Good Luck! Feel free to learn from/reuse the code to build your own indicators.
Wskaźnik Pine Script®
od RagingRocketBull
12
Universal ORB Strategy v2 # Universal ORB **v2** — Pine v6 Strategy Opening Range Breakout strategy with a compact, real-time HUD, risk-managed exits, and guardrails to avoid late entries. --- ## Table of Contents - (#what-it-does) - (#quick-start) - (#recommended-timeframes--sessions) - (#inputs-reference) - (#how-trades-trigger-logic-flow) - (#risk--exits-r-multiples) - (#hud-guide) - (#plots--labels) - (#backtesting-tips) - (#live-trading-tips) - (#troubleshooting) - (#example-presets) - (#user-journey-example) - (#install--run) - (#changelog) - (#disclaimer) --- ## What It Does - Builds an **Opening Range (OR)** from the **first _N_ minutes** after session open. - Goes **long** on breaks **above** OR high; **short** on breaks **below** OR low. - Optional **filters**: - **Minimum OR width** (skip tiny, choppy ranges). - **Volume filter** (current volume must exceed SMA). - **Entry guard** (only allow entries ≤ _X_ bars after the initial break). - **Risk-managed exits**: - Stop based on **ATR × multiplier**. - Two profit targets (**TP1**, **TP2**) at configurable **R multiples** (50/50 split). --- ## Quick Start 1. **Add the script** to a chart in TradingView (see (#install--run)). 2. In **Settings → Inputs**: - Set **Session** (e.g., `0930-1600` for US equities; a daily boundary for crypto). - Set **First N Minutes** (common: **5**, **15**, **30**). - Optionally enable **Volume Filter** and set a **Min OR Width**. 3. Pick a timeframe (1m–15m typical). 4. After the first N minutes, the OR locks. Breakouts that pass filters/guard will **auto-enter** with TP/SL attached. 5. Review results in **Strategy Tester**. Tweak inputs to fit your market. --- ## Recommended Timeframes & Sessions | Market | Session Example | First N Minutes | Chart Timeframe | Notes | |-------------|-------------------|----------------:|----------------:|------| | US Equities | `0930-1600` | 5–30 | **1m–5m** | 5–15m OR is common; 1–3m chart for precision. | | Futures | RTH or chosen day | 5–30 | 1m–5m | Use exchange RTH if you trade regular hours only. | | Crypto | `0000-2359` (or preferred daily reset) | 15–60 | **3m–15m** | 24/7—pick a consistent daily open to define OR. | **Heuristics** - Shorter **N** → more trades, more noise. - Longer **N** → fewer trades, higher average quality. - Increase **Min OR Width** for very volatile symbols. --- ## Inputs Reference ### Opening Range - **Session** (`0930-1600` by default): Trading window that defines each session/day. - **First N Minutes**: Minutes after session open used to build the OR high/low. ### Filters - **Min OR Width (points)**: Minimum acceptable range height; `0` disables. - **Enable Volume Filter**: Requires `volume > SMA(volume, len)`. - **Volume SMA Length**: Period for volume SMA. ### Risk / Targets - **ATR Length**: ATR period for stop calculation. - **ATR ×**: Stop distance multiplier (`stop = ATR × multiplier`). - **TP1 = R Multiple**: First target measured in R (risk units). - **TP2 = R Multiple**: Second target measured in R. ### Entry Guard - **Max Bars After Break**: Reject entries that occur more than _X_ bars after the initial break. ### Visuals - **Plot OR High/Low**: Draw the OR lines (forming vs locked). - **Show Break Labels**: “LONG/SHORT” markers at entries. --- ## How Trades Trigger (Logic Flow) 1. **Build OR** during the first _N_ minutes of the session (track highest high & lowest low). 2. When _N_ minutes pass, **OR locks**. 3. **Detect breakouts**: - **Up-break** when `close` crosses **above** OR high. - **Down-break** when `close` crosses **below** OR low. 4. **Check filters**: - **Min OR Width** (if set) must pass. - **Volume Filter** (if enabled) must pass. - **Entry Guard** must pass (bars since initial break ≤ limit). 5. **Place entry** (long/short) with attached **stop** (ATR-based) and **two targets** (TP1, TP2). 6. **Exit logic**: TP1 and TP2 each close **50%** of the position; stop exits the remainder. --- ## Risk & Exits (R Multiples) - **R** = stop distance (entry price to stop). - Example (long): ATR-based stop is **100** points below entry ⇒ **1R = 100**. - **TP1 @ 1R** = entry + 100 - **TP2 @ 2R** = entry + 200 - Default split: **50%** size to TP1, **50%** to TP2. Tune via **ATR Length**, **ATR ×**, and **TP1/TP2** R settings. --- ## HUD Guide Compact table in the **top-right** with real-time updates on the live bar. | Row | Meaning | |------------|---------| | **Universal ORB v2 / Filters + Guard** | Header | | **OR Mode** | “First N Min” | | **OR Hi / OR Lo** | Current OR levels (forming, then locked) | | **Width** | OR High − OR Low | | **Break** | Direction of the **last** break: Up / Down / None | | **Setup** | Playbook hint: Await Break / Await Retest Long / Await Retest Short | | **Position** | Above Range / Below Range / In Range | | **Min OR** | PASS / FAIL | | **VolFilt** | ON (PASS) / ON (FAIL) / OFF | | **Stop/TP** | Current stop and TP levels (contextual) | | **Guard** | “≤X | now:Y” bars since initial break | --- ## Plots & Labels - **OR High/Low** - Translucent while forming; solid after locking. - **Entry Labels** - “LONG” plotted above bar on up-break entries; “SHORT” below bar on down-break entries. --- ## Backtesting Tips - Use **1m–5m** charts for intraday ORB on equities/futures; **3m–15m** for crypto. - Keep the **Session** aligned with the instrument (RTH vs 24h). - Experiment with: - **First N Minutes**: 5 / 15 / 30 - **ATR ×**: 1.0–2.0 - **Min OR Width**: symbol-specific - **Volume Filter**: often helpful in chop - Evaluate **Net Profit**, **Max Drawdown**, **Win Rate**, and **Trade Count**—avoid over-filtering to zero trades. --- ## Live Trading Tips - Account for **spread & slippage**; avoid targets unrealistically close to entry. - If you find yourself chasing, **tighten Entry Guard**. - If you’re getting chopped, **increase N** and/or **Min OR Width**, and consider enabling **Volume Filter**. - Many traders prefer **“first valid break only”** per session—consider that as a personal rule. --- ## Troubleshooting - **No trades:** - OR might still be forming. - Filters may be blocking (Min Width, Volume, Guard). - Session may not match the instrument/time. - **Too many trades:** - Increase **First N Minutes** or **Min OR Width**; enable **Volume Filter**. - **Labels/lines missing:** - Ensure **Visuals** toggles are on in Settings. --- ## Example Presets **Equities (active, early breakout)** - Session: `0930-1600` - First N Minutes: **5–15** - Timeframe: **1m–3m** - Min OR Width: start around **0.2–0.5%** of price (symbol-dependent) - Volume Filter: **ON**, Length **20** - Entry Guard: **≤ 3 bars** **Futures (RTH breakout)** - Session: Exchange RTH - First N Minutes: **15–30** - Timeframe: **1m–5m** - Volume Filter: ON - ATR ×: **1.5** - TP1/TP2: **1R / 2R** **Crypto (daily OR)** - Session: `0000-2359` (or chosen reset) - First N Minutes: **30–60** - Timeframe: **3m–15m** - Min OR Width: ON (tune per pair) - Volume Filter: optional (exchange-dependent) --- ## User Journey Example **Persona:** Sam, intraday trader on US equities, likes momentum out of the open. 1. **Add to Chart** Sam opens the AAPL 1-minute chart, pastes the script into TradingView Pine Editor, and hits **Add to chart**. 2. **Configure** - Session: `0930-1600` - First N Minutes: `15` - Min OR Width: `0.4` (about ~0.25–0.5% of typical AAPL price) - Volume Filter: **ON**, Length `20` - Entry Guard: `3` bars - ATR ×: `1.5`, TP1 `1R`, TP2 `2R` 3. **Observe the Open** From 9:30 to 9:45, the HUD shows **OR Hi/Lo** forming. After 9:45, the range locks and OR lines turn solid. 4. **First Breakout** Price pops above **OR High** at ~9:47. The HUD shows **Break: Up**. Volume passes; Min Width passes; within 3 bars of the break. Strategy enters **LONG** with ATR stop and two targets. 5. **Trade Management** The HUD line **Stop/TP** displays current SL and both TPs. Sam watches TP1 fill quickly; TP2 trails behind and eventually fills on extension. If price reverses, the ATR stop is there to cap the loss. 6. **Review & Iterate** After the close, Sam checks **Strategy Tester** results. The day looks solid, but on a different symbol the range was too tight, so Sam increases **Min OR Width** to avoid similar chop tomorrow. --- ## Install / Run 1. Open **TradingView → Pine Editor**. 2. Paste the **Universal ORB v2 (Pine v6)** code. 3. Click **Add to chart**. 4. Open **Settings** to configure inputs per your market/timeframe. > The strategy is written for **Pine Script v6**. --- ## Changelog - **v2** - Consolidated docs and naming. - Compact, real-time HUD with clear status rows. - ORB logic with Min Width, Volume filter, and Entry guard. - ATR-based stop and dual TP exits (R-based). --- ## Disclaimer This script is for educational purposes only and **not financial advice**. Markets involve risk. Test thoroughly in a simulator before using on live capital.
Strategia Pine Script®
od mmsecnet
CandelaCharts - 1st Presented FVG 📝 Overview The ICT 1st Presented Fair Value Gap refers to the first FVG that forms after the market opens at 9:30 AM New York local time. In a sideways market, it often acts as a catalyst for price movement in either direction, while in trending conditions, it tends to support and reinforce the prevailing trend. This indicator automatically identifies the first Fair Value Gap (FVG) that forms after the New York session opens at 9:30 AM local time. Based on concepts taught by Inner Circle Trader (ICT), the 1st Presented FVG is a key institutional price imbalance that often sets the tone for the trading day. 📦 Features Customize FVG session time (e.g. 09:30 – 10:00) Show/hide session dividers FVG visibility filter (e.g. Bullish / Bearish) Advanced styling Hide overlapping FVGs Extend FVGs Opening prices ⚙️ Settings Show: Controls whether all, bullish only, or bearish only FVGs are displayed on the chart. Session: Sets a specific time window (e.g. 09:30–10:00) to filter which FVGs are displayed. Dividers: Toggles vertical session divider on the chart for visual separation. Midline: Displays a midpoint (CE) line through the FVG; customizable color and thickness. Border: Adds a border around each FVG zone. Labels: Toggles label display for FVGs. Hide Overlap: Hides overlapping FVGs to reduce visual clutter. Extend: Extends each FVG forward in time. Alerts: Enables alerts when price interacts with an FVG zone. Opening Prices: Allows defining custom time-based levels (e.g. 00:00–00:01 and 18:00–18:01) with color and style options. ⚡️ Showcase Simple Labels Bordered Consequent Encroachment Extended Dividers 📒 Usage How to Use the ICT 1st Presented Fair Value Gap in Trading To apply the ICT 1st Presented Fair Value Gap (FVG), identify the first fair value gap of the day and extend it across the chart until 3:45 PM New York time. You’ll often notice that some of the best trade setups form around this level. It tends to act as a key reference point for price action during the day—especially on trending days, where price frequently returns to this gap before continuing in its direction. This level can also serve as an inverse fair value gap, offering opportunities in the opposite direction under the right conditions. How to Disqualify the 1st Presented Fair Value Gap? When the first fair value gap forms after 9:30 AM New York time, check the candles that came just before it. If the candlestick that creates the FVG doesn’t break above or below the range of those previous candles, then it’s not a true inefficiency. In that case, it’s considered a disqualified 1st Presented Fair Value Gap—meaning it shouldn’t be used as a key reference level. Refer to the example below to see what this looks like on the chart. 🚨 Alerts This script provides alert options for all signals. Bearish Signal A bearish signal is triggered when the bearish 1st P.FVG is formed in interval 09:30 - 10:00. Bullish Signal A bullish signal is triggered when the bullish 1st P.FVG is formed in interval 09:30 - 10:00. ⚠️ Disclaimer Trading involves significant risk, and many participants may incur losses. The content on this site is not intended as financial advice and should not be interpreted as such. Decisions to buy, sell, hold, or trade securities, commodities, or other financial instruments carry inherent risks and are best made with guidance from qualified financial professionals. Past performance is not indicative of future results.
Wskaźnik Pine Script®
od CandelaCharts
FVG Premium [no1x]█ OVERVIEW This indicator provides a comprehensive toolkit for identifying, visualizing, and tracking Fair Value Gaps (FVGs) across three distinct timeframes (current chart, a user-defined Medium Timeframe - MTF, and a user-defined High Timeframe - HTF). It is designed to offer traders enhanced insight into FVG dynamics through detailed state monitoring (formation, partial fill, full mitigation, midline touch), extensive visual customization for FVG representation, and a rich alert system for timely notifications on FVG-related events. █ CONCEPTS This indicator is built upon the core concept of Fair Value Gaps (FVGs) and their significance in price action analysis, offering a multi-layered approach to their detection and interpretation across different timeframes. Fair Value Gaps (FVGs) A Fair Value Gap (FVG), also known as an imbalance, represents a range in price delivery where one side of the market (buying or selling) was more aggressive, leaving an inefficiency or an "imbalance" in the price action. This concept is prominently featured within Smart Money Concepts (SMC) and Inner Circle Trader (ICT) methodologies, where such gaps are often interpreted as footprints left by "smart money" due to rapid, forceful price movements. These methodologies suggest that price may later revisit these FVG zones to rebalance a prior inefficiency or to seek liquidity before continuing its path. These gaps are typically identified by a three-bar pattern: Bullish FVG : This is a three-candle formation where the second candle shows a strong upward move. The FVG is the space created between the high of the first candle (bottom of FVG) and the low of the third candle (top of FVG). This indicates a strong upward impulsive move. Bearish FVG : This is a three-candle formation where the second candle shows a strong downward move. The FVG is the space created between the low of the first candle (top of FVG) and the high of the third candle (bottom of FVG). This indicates a strong downward impulsive move. FVGs are often watched by traders as potential areas where price might return to "rebalance" or find support/resistance. Multi-Timeframe (MTF) Analysis The indicator extends FVG detection beyond the current chart's timeframe (Low Timeframe - LTF) to two higher user-defined timeframes: Medium Timeframe (MTF) and High Timeframe (HTF). This allows traders to: Identify FVGs that might be significant on a broader market structure. Observe how FVGs from different timeframes align or interact. Gain a more comprehensive perspective on potential support and resistance zones. FVG State and Lifecycle Management The indicator actively tracks the lifecycle of each detected FVG: Formation : The initial identification of an FVG. Partial Fill (Entry) : When price enters but does not completely pass through the FVG. The indicator updates the "current" top/bottom of the FVG to reflect the filled portion. Midline (Equilibrium) Touch : When price touches the 50% level of the FVG. Full Mitigation : When price completely trades through the FVG, effectively "filling" or "rebalancing" the gap. The indicator records the mitigation time. This state tracking is crucial for understanding how price interacts with these zones. FVG Classification (Large FVG) FVGs can be optionally classified as "Large FVGs" (LV) if their size (top to bottom range) exceeds a user-defined multiple of the Average True Range (ATR) for that FVG's timeframe. This helps distinguish FVGs that are significantly larger relative to recent volatility. Visual Customization and Information Delivery A key concept is providing extensive control over how FVGs are displayed. This control is achieved through a centralized set of visual parameters within the indicator, allowing users to configure numerous aspects (colors, line styles, visibility of boxes, midlines, mitigation lines, labels, etc.) for each timeframe. Additionally, an on-chart information panel summarizes the nearest unmitigated bullish and bearish FVG levels for each active timeframe, providing a quick glance at key price points. █ FEATURES This indicator offers a rich set of features designed to provide a highly customizable and comprehensive Fair Value Gap (FVG) analysis experience. Users can tailor the FVG detection, visual representation, and alerting mechanisms across three distinct timeframes: the current chart (Low Timeframe - LTF), a user-defined Medium Timeframe (MTF), and a user-defined High Timeframe (HTF). Multi-Timeframe FVG Detection and Display The core strength of this indicator lies in its ability to identify and display FVGs from not only the current chart's timeframe (LTF) but also from two higher, user-selectable timeframes (MTF and HTF). Timeframe Selection: Users can specify the exact MTF (e.g., "60", "240") and HTF (e.g., "D", "W") through dedicated inputs in the "MTF (Medium Timeframe)" and "HTF (High Timeframe)" settings groups. The visibility of FVGs from these higher timeframes can be toggled independently using the "Show MTF FVGs" and "Show HTF FVGs" checkboxes. Consistent Detection Logic: The FVG detection logic, based on the classic three-bar imbalance pattern detailed in the 'Concepts' section, is applied consistently across all selected timeframes (LTF, MTF, HTF) Timeframe-Specific Visuals: Each timeframe's FVGs (LTF, MTF, HTF) can be customized with unique colors for bullish/bearish states and their mitigated counterparts. This allows for easy visual differentiation of FVGs originating from different market perspectives. Comprehensive FVG Visualization Options The indicator provides extensive control over how FVGs are visually represented on the chart for each timeframe (LTF, MTF, HTF). FVG Boxes: Visibility: Main FVG boxes can be shown or hidden per timeframe using the "Show FVG Boxes" (for LTF), "Show Boxes" (for MTF/HTF) inputs. Color Customization: Colors for bullish, bearish, active, and mitigated FVG boxes (including Large FVGs, if classified) are fully customizable for each timeframe. Box Extension & Length: FVG boxes can either be extended to the right indefinitely ("Extend Boxes Right") or set to a fixed length in bars ("Short Box Length" or "Box Length" equivalent inputs). Box Labels: Optional labels can display the FVG's timeframe and fill percentage on the box. These labels are configurable for all timeframes (LTF, MTF, and HTF). Please note: If FVGs are positioned very close to each other on the chart, their respective labels may overlap. This can potentially lead to visual clutter, and it is a known behavior in the current version of the indicator. Box Borders: Visibility, width, style (solid, dashed, dotted), and color of FVG box borders are customizable per timeframe. Midlines (Equilibrium/EQ): Visibility: The 50% level (midline or EQ) of FVGs can be shown or hidden for each timeframe. Style Customization: Width, style, and color of the midline are customizable per timeframe. The indicator tracks if this midline has been touched by price. Mitigation Lines: Visibility: Mitigation lines (representing the FVG's opening level that needs to be breached for full mitigation) can be shown or hidden for each timeframe. If shown, these lines are always extended to the right. Style Customization: Width, style, and color of the mitigation line are customizable per timeframe. Mitigation Line Labels: Optional price labels can be displayed on mitigation lines, with a customizable horizontal bar offset for positioning. For optimal label placement, the following horizontal bar offsets are recommended: 4 for LTF, 8 for MTF, and 12 for HTF. Persistence After Mitigation: Users can choose to keep mitigation lines visible even after an FVG is fully mitigated, with a distinct color for such lines. Importantly, this option is only effective if the general setting 'Hide Fully Mitigated FVGs' is disabled, as otherwise, the entire FVG and its lines will be removed upon mitigation. FVG State Management and Behavior The indicator tracks and visually responds to changes in FVG states. Hide Fully Mitigated FVGs: This option, typically found in the indicator's general settings, allows users to automatically remove all visual elements of an FVG from the chart once price has fully mitigated it. This helps maintain chart clarity by focusing on active FVGs. Partial Fill Visualization: When price enters an FVG, the indicator offers a dynamic visual representation: the portion of the FVG that has been filled is shown as a "mitigated box" (typically with a distinct color), while the original FVG box shrinks to clearly highlight the remaining, unfilled portion. This two-part display provides an immediate visual cue about how much of the FVG's imbalance has been addressed and what potential remains within the gap. Visual Filtering by ATR Proximity: To help users focus on the most relevant price action, FVGs can be dynamically hidden if they are located further from the current price than a user-defined multiple of the Average True Range (ATR). This behavior is controlled by the "Filter Band Width (ATR Multiple)" input; setting this to zero disables the filter entirely, ensuring all detected FVGs remain visible regardless of their proximity to price. Alternative Usage Example: Mitigation Lines as Key Support/Resistance Levels For traders preferring a minimalist chart focused on key Fair Value Gap (FVG) levels, the indicator's visualization settings can be customized to display only FVG mitigation lines. This approach leverages these lines as potential support and resistance zones, reflecting areas where price might revisit to address imbalances. To configure this view: Disable FVG Boxes: Turn off "Show FVG Boxes" (for LTF) or "Show Boxes" (for MTF/HTF) for the desired timeframes. Hide Midlines: Disable the visibility of the 50% FVG Midlines (Equilibrium/EQ). Ensure Mitigation Lines are Visible: Keep "Mitigation Lines" enabled. Retain All Mitigation Lines: Disable the "Hide Fully Mitigated FVGs" option in the general settings. Enable the feature to "keep mitigation lines visible even after an FVG is fully mitigated". This ensures lines from all FVGs (active or fully mitigated) remain on the chart, which is only effective if "Hide Fully Mitigated FVGs" is disabled. This setup offers: A Decluttered Chart: Focuses solely on the FVG opening levels. Precise S/R Zones: Treats mitigation lines as specific points for potential price reactions. Historical Level Analysis: Includes lines from past, fully mitigated FVGs for a comprehensive view of significant price levels. For enhanced usability with this focused view, consider these optional additions: The on-chart Information Panel can be activated to display a quick summary of the nearest unmitigated FVG levels. Mitigation Line Labels can also be activated for clear price level identification. A customizable horizontal bar offset is available for positioning these labels; for example, offsets of 4 for LTF, 8 for MTF, and 12 for HTF can be effective. FVG Classification (Large FVG) This feature allows for distinguishing FVGs based on their size relative to market volatility. Enable Classification: Users can enable "Classify FVG (Large FVG)" to identify FVGs that are significantly larger than average. ATR-Based Threshold: An FVG is classified as "Large" if its height (price range) is greater than or equal to the Average True Range (ATR) of its timeframe multiplied by a user-defined "Large FVG Threshold (ATR Multiple)". The ATR period for this calculation is also configurable. Dedicated Colors: Large FVGs (both bullish/bearish and active/mitigated) can be assigned unique colors, making them easily distinguishable on the chart. Panel Icon: Large FVGs are marked with a special icon in the Info Panel. Information Panel An on-chart panel provides a quick summary of the nearest unmitigated FVG levels. Visibility and Position: The panel can be shown/hidden and positioned in any of the nine standard locations on the chart (e.g., Top Right, Middle Center). Content: It displays the price levels of the nearest unmitigated bullish and bearish FVGs for LTF, MTF (if active), and HTF (if active). It also indicates if these nearest FVGs are Large FVGs (if classification is enabled) using a selectable icon. Styling: Text size, border color, header background/text colors, default text color, and "N/A" cell background color are customizable. Highlighting: Background and text colors for the cells displaying the overall nearest bullish and bearish FVG levels (across all active timeframes) can be customized to draw attention to the most proximate FVG. Comprehensive Alert System The indicator offers a granular alert system for various FVG-related events, configurable for each timeframe (LTF, MTF, HTF) independently. Users can enable alerts for: New FVG Formation: Separate alerts for new bullish and new bearish FVG formations. FVG Entry/Partial Fill: Separate alerts for price entering a bullish FVG or a bearish FVG. FVG Full Mitigation: Separate alerts for full mitigation of bullish and bearish FVGs. FVG Midline (EQ) Touch: Separate alerts for price touching the midline of a bullish or bearish FVG. Alert messages are detailed, providing information such as the timeframe, FVG type (bull/bear, Large FVG), relevant price levels, and timestamps. █ NOTES This section provides additional information regarding the indicator's usage, performance considerations, and potential interactions with the TradingView platform. Understanding these points can help users optimize their experience and troubleshoot effectively. Performance and Resource Management Maximum FVGs to Track : The "Max FVGs to Track" input (defaulting to 25) limits the number of FVG objects processed for each category (e.g., LTF Bullish, MTF Bearish). Increasing this value significantly can impact performance due to more objects being iterated over and potentially drawn, especially when multiple timeframes are active. Drawing Object Limits : To manage performance, this script sets its own internal limits on the number of drawing objects it displays. While it allows for up to approximately 500 lines (max_lines_count=500) and 500 labels (max_labels_count=500), the number of FVG boxes is deliberately restricted to a maximum of 150 (max_boxes_count=150). This specific limit for boxes is a key performance consideration: displaying too many boxes can significantly slow down the indicator, and a very high number is often not essential for analysis. Enabling all visual elements for many FVGs across all three timeframes can cause the indicator to reach these internal limits, especially the stricter box limit Optimization Strategies : To help you manage performance, reduce visual clutter, and avoid exceeding drawing limits when using this indicator, I recommend the following strategies: Maintain or Lower FVG Tracking Count: The "Max FVGs to Track" input defaults to 25. I find this value generally sufficient for effective analysis and balanced performance. You can keep this default or consider reducing it further if you experience performance issues or prefer a less dense FVG display. Utilize Proximity Filtering: I suggest activating the "Filter Band Width (ATR Multiple)" option (found under "General Settings") to display only those FVGs closer to the current price. From my experience, a value of 5 for the ATR multiple often provides a good starting point for balanced performance, but you should feel free to adjust this based on market volatility and your specific trading needs. Hide Fully Mitigated FVGs: I strongly recommend enabling the "Hide Fully Mitigated FVGs" option. This setting automatically removes all visual elements of an FVG from the chart once it has been fully mitigated by price. Doing so significantly reduces the number of active drawing objects, lessens computational load, and helps maintain chart clarity by focusing only on active, relevant FVGs. Disable FVG Display for Unused Timeframes: If you are not actively monitoring certain higher timeframes (MTF or HTF) for FVG analysis, I advise disabling their display by unchecking "Show MTF FVGs" or "Show HTF FVGs" respectively. This can provide a significant performance boost. Simplify Visual Elements: For active FVGs, consider hiding less critical visual elements if they are not essential for your specific analysis. This could include box labels, borders, or even entire FVG boxes if, for example, only the mitigation lines are of interest for a particular timeframe. Settings Changes and Platform Limits : This indicator is comprehensive and involves numerous calculations and drawings. When multiple settings are changed rapidly in quick succession, it is possible, on occasion, for TradingView to issue a "Runtime error: modify_study_limit_exceeding" or similar. This can cause the indicator to temporarily stop updating or display errors. Recommended Approach : When adjusting settings, it is advisable to wait a brief moment (a few seconds) after each significant change. This allows the indicator to reprocess and update on the chart before another change is made Error Recovery : Should such a runtime error occur, making a minor, different adjustment in the settings (e.g., toggling a checkbox off and then on again) and waiting briefly will typically allow the indicator to recover and resume correct operation. This behavior is related to platform limitations when handling complex scripts with many inputs and drawing objects. Multi-Timeframe (MTF/HTF) Data and Behavior HTF FVG Confirmation is Essential: : For an FVG from a higher timeframe (MTF or HTF) to be identified and displayed on your current chart (LTF), the three-bar pattern forming the FVG on that higher timeframe must consist of fully closed bars. The indicator does not draw speculative FVGs based on incomplete/forming bars from higher timeframes. Data Retrieval and LTF Processing: The indicator may use techniques like lookahead = barmerge.lookahead_on for timely data retrieval from higher timeframes. However, the actual detection of an FVG occurs after all its constituent bars on the HTF have closed. Appearance Timing on LTF (1 LTF Candle Delay): As a natural consequence of this, an FVG that is confirmed on an HTF (i.e., its third bar closes) will typically become visible on your LTF chart one LTF bar after its confirmation on the HTF. Example: Assume an FVG forms on a 30-minute chart at 15:30 (i.e., with the close of the 30-minute bar that covers the 15:00-15:30 period). If you are monitoring this FVG on a 15-minute chart, the indicator will detect this newly formed 30-minute FVG while processing the data for the 15-minute bar that starts at 15:30 and closes at 15:45. Therefore, the 30-minute FVG will become visible on your 15-minute chart at the earliest by 15:45 (i.e., with the close of that relevant 15-minute LTF candle). This means the HTF FVG is reflected on the LTF chart with a delay equivalent to one LTF candle. FVG Detection and Display Logic Fair Value Gaps (FVGs) on the current chart timeframe (LTF) are detected based on barstate.isconfirmed. This means the three-bar pattern must be complete with closed bars before an FVG is identified. This confirmation method prevents FVGs from being prematurely identified on the forming bar. Alerts Alert Setup : To receive alerts from this indicator, you must first ensure you have enabled the specific alert conditions you are interested in within the indicator's own settings (see 'Comprehensive Alert System' under the 'FEATURES' section). Once configured, open TradingView's 'Create Alert' dialog. In the 'Condition' tab, select this indicator's name, and crucially, choose the 'Any alert() function call' option from the dropdown list. This setup allows the indicator to trigger alerts based on the precise event conditions you have activated in its settings Alert Frequency : Alerts are designed to trigger once per bar close (alert.freq_once_per_bar_close) for the specific event. User Interface (UI) Tips Settings Group Icons: In the indicator settings menu, timeframe-specific groups are marked with star icons for easier navigation: 🌟 for LTF (Current Chart Timeframe), 🌟🌟 for MTF (Medium Timeframe), and 🌟🌟🌟 for HTF (High Timeframe). Dependent Inputs: Some input settings are dependent on others being enabled. These dependencies are visually indicated in the settings menu using symbols like "↳" (dependent setting on the next line), "⟷" (mutually exclusive inline options), or "➜" (directly dependent inline option). Settings Layout Overview: The indicator settings are organized into logical groups for ease of use. Key global display controls – such as toggles for MTF FVGs, HTF FVGs (along with their respective timeframe selectors), and the Information Panel – are conveniently located at the very top within the '⚙️ General Settings' group. This placement allows for quick access to frequently adjusted settings. Other sections provide detailed customization options for each timeframe (LTF, MTF, HTF), specific FVG components, and alert configurations. █ FOR Pine Script® CODERS This section provides a high-level overview of the FVG Premium indicator's internal architecture, data flow, and the interaction between its various library components. It is intended for Pine Script™ programmers who wish to understand the indicator's design, potentially extend its functionality, or learn from its structure. System Architecture and Modular Design The indicator is architected moduarly, leveraging several custom libraries to separate concerns and enhance code organization and reusability. Each library has a distinct responsibility: FvgTypes: Serves as the foundational data definition layer. It defines core User-Defined Types (UDTs) like fvgObject (for storing all attributes of an FVG) and drawSettings (for visual configurations), along with enumerations like tfType. CommonUtils: Provides utility functions for common tasks like mapping user string inputs (e.g., "Dashed" for line style) to their corresponding Pine Script™ constants (e.g., line.style_dashed) and formatting timeframe strings for display. FvgCalculations: Contains the core logic for FVG detection (both LTF and MTF/HTF via requestMultiTFBarData), FVG classification (Large FVGs based on ATR), and checking FVG interactions with price (mitigation, partial fill). FvgObject: Implements an object-oriented approach by attaching methods to the fvgObject UDT. These methods manage the entire visual lifecycle of an FVG on the chart, including drawing, updating based on state changes (e.g., mitigation), and deleting drawing objects. It's responsible for applying the visual configurations defined in drawSettings. FvgPanel: Manages the creation and dynamic updates of the on-chart information panel, which displays key FVG levels. The main indicator script acts as the orchestrator, initializing these libraries, managing user inputs, processing data flow between libraries, and handling the main event loop (bar updates) for FVG state management and alerts. Core Data Flow and FVG Lifecycle Management The general data flow and FVG lifecycle can be summarized as follows: Input Processing: User inputs from the "Settings" dialog are read by the main indicator script. Visual style inputs (colors, line styles, etc.) are consolidated into a types.drawSettings object (defined in FvgTypes). Other inputs (timeframes, filter settings, alert toggles) control the behavior of different modules. CommonUtils assists in mapping some string inputs to Pine constants. FVG Detection: For the current chart timeframe (LTF), FvgCalculations.detectFvg() identifies potential FVGs based on bar patterns. For MTF/HTF, the main indicator script calls FvgCalculations.requestMultiTFBarData() to fetch necessary bar data from higher timeframes, then FvgCalculations.detectMultiTFFvg() identifies FVGs. Newly detected FVGs are instantiated as types.fvgObject and stored in arrays within the main script. These objects also undergo classification (e.g., Large FVG) by FvgCalculations. State Update & Interaction: On each bar, the main indicator script iterates through active FVG objects to manage their state based on price interaction: Initially, the main script calls FvgCalculations.fvgInteractionCheck() to efficiently determine if the current bar's price might be interacting with a given FVG. If a potential interaction is flagged, the main script then invokes methods directly on the fvgObject instance (e.g., updateMitigation(), updatePartialFill(), checkMidlineTouch(), which are part of FvgObject). These fvgObject methods are responsible for the detailed condition checking and the actual modification of the FVG's state. For instance, the updateMitigation() and updatePartialFill() methods internally utilize specific helper functions from FvgCalculations (like checkMitigation() and checkPartialMitigation()) to confirm the precise nature of the interaction before updating the fvgObject’s state fields (such as isMitigated, currentTop, currentBottom, or isMidlineTouched). Visual Rendering: The FvgObject.updateDrawings() method is called for each fvgObject. This method is central to drawing management; it creates, updates, or deletes chart drawings (boxes, lines, labels) based on the FVG's current state, its prev_* (previous bar state) fields for optimization, and the visual settings passed via the drawSettings object. Information Panel Update: The main indicator script determines the nearest FVG levels, populates a panelData object (defined in FvgPanelLib), and calls FvgPanel.updatePanel() to refresh the on-chart display. Alert Generation: Based on the updated FVG states and user-enabled alert settings, the main indicator script constructs and triggers alerts using Pine Script's alert() function." Key Design Considerations UDT-Centric Design: The fvgObject UDT is pivotal, acting as a stateful container for all information related to a single FVG. Most operations revolve around creating, updating, or querying these objects. State Management: To optimize drawing updates and manage FVG lifecycles, fvgObject instances store their previous bar's state (e.g., prevIsVisible, prevCurrentTop). The FvgObject.updateDrawings() method uses this to determine if a redraw is necessary, minimizing redundant drawing calls. Settings Object: A drawSettings object is populated once (or when inputs change) and passed to drawing functions. This avoids repeatedly reading numerous input() values on every bar or within loops, improving performance. Dynamic Arrays for FVG Storage: Arrays are used to store collections of fvgObject instances, allowing for dynamic management (adding new FVGs, iterating for updates).
Wskaźnik Pine Script®
od no1x
20
ICT Opening Range Projections (tristanlee85)ICT Opening Range Projections This indicator visualizes key price levels based on ICT's (Inner Circle Trader) "Opening Range" concept. This 30-minute time interval establishes price levels that the algorithm will refer to throughout the session. The indicator displays these levels, including standard deviation projections, internal subdivisions (quadrants), and the opening price. 🟪 What It Does The Opening Range is a crucial 30-minute window where market algorithms establish significant price levels. ICT theory suggests this range forms the basis for daily price movement. This script helps you: Mark the high, low, and opening price of each session. Divide the range into quadrants (premium, discount, and midpoint/Consequent Encroachment). Project potential price targets beyond the range using configurable standard deviation multiples . 🟪 How to Use It This tool aids in time-based technical analysis rooted in ICT's Opening Range model, helping you observe price interaction with algorithmic levels. Example uses include: Identifying early structural boundaries. Observing price behavior within premium/discount zones. Visualizing initial displacement from the range to anticipate future moves. Comparing price reactions at projected standard deviation levels. Aligning price action with significant times like London or NY Open. Note: This indicator provides a visual framework; it does not offer trade signals or interpretations. 🟪 Key Information Time Zone: New York time (ET) is required on your chart. Sessions: Supports multiple sessions, including NY midnight, NY AM, NY PM, and three custom timeframes. Time Interval: Supports multi-timeframe up to 15 minutes. Best used on a 1-minute chart for accuracy. 🟪 Session Options The Opening Range interval is configurable for up to 6 sessions: Pre-defined ICT Sessions: NY Midnight: 12:00 AM – 12:30 AM ET NY AM: 9:30 AM – 10:00 AM ET NY PM: 1:30 PM – 2:00 PM ET Custom Sessions: Three user-defined start/end time pairs. This example shows a custom session from 03:30 - 04:00: 🟪 Understanding the Levels The Opening Price is the open of the first 1-minute candle within the chosen session. At session close, the Opening Range is calculated using its High and Low . An optional swing-based mode uses swing highs/lows for range boundaries. The range is divided into quadrants by its midpoint ( Consequent Encroachment or CE): Upper Quadrant: CE to high (premium). Lower Quadrant: Low to CE (discount). These subdivisions help visualize internal range dynamics, where price often reacts during algorithmic delivery. 🟪 Working with Ranges By default, the range is determined by the highest high and lowest low of the 30-minute session: A range can also be determined by the highest/lowest swing points: Quadrants outline the premium and discount of a range that price will reference: Small ranges still follow the same algorithmic logic, but may be deemed insignificant for one's trading. These can be filtered in the settings by specifying a minimum ticks limit. In this example, the range is 42 ticks (10.5 points) but the indicator is configured for 80 ticks (20 points). We can select which levels will plot if the range is below the limit. Here, only the 00:00 opening price is plotted: You may opt to include the range high/low, quadrants, and projections as well. This will plot a red (configurable) range bracket to indicate it is below the limit while plotting the levels: 🟪 Price Projections Projections extend beyond the Opening Range using standard deviations, framing the market beyond the initial session and identifying potential targets. You define the standard deviation multiples (e.g., 1.0, 1.5, 2.0). Both positive and negative extensions are displayed, symmetrically projected from the range's high and low. The Dynamic Levels option plots only the next projection level once price crosses the previous extreme. For example, only the 0.5 STDEV level plots until price reaches it, then the 1.0 level appears, and so on. This continues up to your defined maximum projections, or indefinitely if standard deviations are set to 0. This example shows dynamic levels for a total of 6 sessions, only 1 of which meet a configured minimum limit of 50 ticks: Small ranges followed by significant displacement are impacted the most with the number of levels plotted. You may hide projections when configuring the minimum ticks. A fixed standard deviation will plot levels in both directions, regardless of the price range. Here, we plot up to 3.0 which hiding projections for small ranges: 🟪 Legal Disclaimer This indicator is provided for informational and educational purposes only. It is not financial advice, and should not be construed as a recommendation to buy or sell any financial instrument. Trading involves substantial risk, and you could lose a significant amount of money. Past performance is not indicative of future results. Always consult with a qualified financial professional before making any trading or investment decisions. The creators and distributors of this indicator assume no responsibility for your trading outcomes.
Wskaźnik Pine Script®
od tristanlee85
6
Killzones (UTC+3) by Roy⏰ Time-Based Division – Trading Quarters: The trading day is divided into four main quarters, each reflecting distinct market behaviours: Opo Finance Blog Quarter Time (Israel Time) Description Q1 16:30–18:30 Wall Street opening; highest volatility. Q2 18:30–20:30 Continuation or correction of the opening move. Q3 20:30–22:30 Quieter market; often characterized by consolidation. Q4 22:30–24:00 Preparation for market close; potential breakouts or sharp movements. This framework assists traders in anticipating market dynamics within each quarter, enhancing decision-making by aligning strategies with typical intraday patterns.
Wskaźnik Pine Script®
od RSC2019BB
Volume Weighted RSI (VW RSI)The Volume Weighted RSI (VW RSI) is a momentum oscillator designed for TradingView, implemented in Pine Script v6, that enhances the traditional Relative Strength Index (RSI) by incorporating trading volume into its calculation. Unlike the standard RSI, which measures the speed and change of price movements based solely on price data, the VW RSI weights its analysis by volume, emphasizing price movements backed by significant trading activity. This makes the VW RSI particularly effective for identifying bullish or bearish momentum, overbought/oversold conditions, and potential trend reversals in markets where volume plays a critical role, such as stocks, forex, and cryptocurrencies. Key Features Volume-Weighted Momentum Calculation: The VW RSI calculates momentum by comparing the volume associated with upward price movements (up-volume) to the volume associated with downward price movements (down-volume). Up-volume is the volume on bars where the closing price is higher than the previous close, while down-volume is the volume on bars where the closing price is lower than the previous close. These volumes are smoothed over a user-defined period (default: 14 bars) using a Running Moving Average (RMA), and the VW RSI is computed using the formula: \text{VW RSI} = 100 - \frac{100}{1 + \text{VoRS}} where \text{VoRS} = \frac{\text{Average Up-Volume}}{\text{Average Down-Volume}} . Oscillator Range and Interpretation: The VW RSI oscillates between 0 and 100, with a centerline at 50. Above 50: Indicates bullish volume momentum, suggesting that volume on up bars dominates, which may signal buying pressure and a potential uptrend. Below 50: Indicates bearish volume momentum, suggesting that volume on down bars dominates, which may signal selling pressure and a potential downtrend. Overbought/Oversold Levels: User-defined thresholds (default: 70 for overbought, 30 for oversold) help identify potential reversal points: VW RSI > 70: Overbought, indicating a possible pullback or reversal. VW RSI < 30: Oversold, indicating a possible bounce or reversal. Visual Elements: VW RSI Line: Plotted in a separate pane below the price chart, colored dynamically based on its value: Green when above 50 (bullish momentum). Red when below 50 (bearish momentum). Gray when at 50 (neutral). Centerline: A dashed line at 50, optionally displayed, serving as the neutral threshold between bullish and bearish momentum. Overbought/Oversold Lines: Dashed lines at the user-defined overbought (default: 70) and oversold (default: 30) levels, optionally displayed, to highlight extreme conditions. Background Coloring: The background of the VW RSI pane is shaded red when the indicator is in overbought territory and green when in oversold territory, providing a quick visual cue of potential reversal zones. Alerts: Built-in alerts for key events: Bullish Momentum: Triggered when the VW RSI crosses above 50, indicating a shift to bullish volume momentum. Bearish Momentum: Triggered when the VW RSI crosses below 50, indicating a shift to bearish volume momentum. Overbought Condition: Triggered when the VW RSI crosses above the overbought threshold (default: 70), signaling a potential pullback. Oversold Condition: Triggered when the VW RSI crosses below the oversold threshold (default: 30), signaling a potential bounce. Input Parameters VW RSI Length (default: 14): The period over which the up-volume and down-volume are smoothed to calculate the VW RSI. A longer period results in smoother signals, while a shorter period increases sensitivity. Overbought Level (default: 70): The threshold above which the VW RSI is considered overbought, indicating a potential reversal or pullback. Oversold Level (default: 30): The threshold below which the VW RSI is considered oversold, indicating a potential reversal or bounce. Show Centerline (default: true): Toggles the display of the 50 centerline, which separates bullish and bearish momentum zones. Show Overbought/Oversold Lines (default: true): Toggles the display of the overbought and oversold threshold lines. How It Works Volume Classification: For each bar, the indicator determines whether the price movement is upward or downward: If the current close is higher than the previous close, the bar’s volume is classified as up-volume. If the current close is lower than the previous close, the bar’s volume is classified as down-volume. If the close is unchanged, both up-volume and down-volume are set to 0 for that bar. Smoothing: The up-volume and down-volume are smoothed using a Running Moving Average (RMA) over the specified period (default: 14 bars) to reduce noise and provide a more stable measure of volume momentum. VW RSI Calculation: The Volume Relative Strength (VoRS) is calculated as the ratio of smoothed up-volume to smoothed down-volume. The VW RSI is then computed using the standard RSI formula, but with volume data instead of price changes, resulting in a value between 0 and 100. Visualization and Alerts: The VW RSI is plotted with dynamic coloring to reflect its momentum direction, and optional lines are drawn for the centerline and overbought/oversold levels. Background coloring highlights overbought and oversold conditions, and alerts notify the trader of significant crossings. Usage Timeframe: The VW RSI can be used on any timeframe, but it is particularly effective on intraday charts (e.g., 1-hour, 4-hour) or daily charts where volume data is reliable. Shorter timeframes may require a shorter length for increased sensitivity, while longer timeframes may benefit from a longer length for smoother signals. Markets: Best suited for markets with significant and reliable volume data, such as stocks, forex, and cryptocurrencies. It may be less effective in markets with low or inconsistent volume, such as certain futures contracts. Trading Strategies: Trend Confirmation: Use the VW RSI to confirm the direction of a trend. For example, in an uptrend, look for the VW RSI to remain above 50, indicating sustained bullish volume momentum, and consider buying on pullbacks when the VW RSI dips but stays above 50. In a downtrend, look for the VW RSI to remain below 50, indicating sustained bearish volume momentum, and consider selling on rallies when the VW RSI rises but stays below 50. Overbought/Oversold Conditions: When the VW RSI crosses above 70, the market may be overbought, suggesting a potential pullback or reversal. Consider taking profits on long positions or preparing for a short entry, but confirm with price action or other indicators. When the VW RSI crosses below 30, the market may be oversold, suggesting a potential bounce or reversal. Consider entering long positions or covering shorts, but confirm with additional signals. Divergences: Look for divergences between the VW RSI and price to spot potential reversals. For example, if the price makes a higher high but the VW RSI makes a lower high, this bearish divergence may signal an impending downtrend. Conversely, if the price makes a lower low but the VW RSI makes a higher low, this bullish divergence may signal an impending uptrend. Momentum Shifts: A crossover above 50 can signal the start of bullish momentum, making it a potential entry point for long trades. A crossunder below 50 can signal the start of bearish momentum, making it a potential entry point for short trades or an exit for long positions. Example On a 4-hour SOLUSDT chart: During an uptrend, the VW RSI might rise above 50 and stay there, confirming bullish volume momentum. If it approaches 70, it may indicate overbought conditions, as seen near a price peak of 145.08, suggesting a potential pullback. During a downtrend, the VW RSI might fall below 50, confirming bearish volume momentum. If it drops below 30 near a price low of 141.82, it may indicate oversold conditions, suggesting a potential bounce, as seen in a slight recovery afterward. A bullish divergence might occur if the price makes a lower low during the downtrend, but the VW RSI makes a higher low, signaling a potential reversal. Limitations Lagging Nature: Like the traditional RSI, the VW RSI is a lagging indicator because it relies on smoothed data (RMA). It may not react quickly to sudden price reversals, potentially missing the start of new trends. False Signals in Ranging Markets: In choppy or ranging markets, the VW RSI may oscillate around 50, generating frequent crossovers that lead to false signals. Combining it with a trend filter (e.g., ADX) can help mitigate this. Volume Data Dependency: The VW RSI relies on accurate volume data, which may be inconsistent or unavailable in some markets (e.g., certain forex pairs or futures contracts). In such cases, the indicator’s effectiveness may be reduced. Overbought/Oversold in Strong Trends: During strong trends, the VW RSI can remain in overbought or oversold territory for extended periods, leading to premature exit signals. Use additional confirmation to avoid exiting too early. Potential Improvements Smoothing Options: Add options to use different smoothing methods (e.g., EMA, SMA) instead of RMA for the up/down volume calculations, allowing users to adjust the indicator’s responsiveness. Divergence Detection: Include logic to detect and plot bullish/bearish divergences between the VW RSI and price, providing visual cues for potential reversals. Customizable Colors: Allow users to customize the colors of the VW RSI line, centerline, overbought/oversold lines, and background shading. Trend Filter: Integrate a trend strength filter (e.g., ADX > 25) to ensure signals are generated only during strong trends, reducing false signals in ranging markets. The Volume Weighted RSI (VW RSI) is a powerful tool for traders seeking to incorporate volume into their momentum analysis, offering a unique perspective on market dynamics by emphasizing price movements backed by significant trading activity. It is best used in conjunction with other indicators and price action analysis to confirm signals and improve trading decisions.
Wskaźnik Pine Script®
od CsokosGeza
StatPivot- Dynamic Range Analyzer - indicator [PresentTrading]Hello everyone! In the following few open scripts, I would like to share various statistical tools that benefit trading. For this time, it is a powerful indicator called StatPivot- Dynamic Range Analyzer that brings a whole new dimension to your technical analysis toolkit. This tool goes beyond traditional pivot point analysis by providing comprehensive statistical insights about price movements, helping you identify high-probability trading opportunities based on historical data patterns rather than subjective interpretations. Whether you're a day trader, swing trader, or position trader, StatPivot's real-time percentile rankings give you a statistical edge in understanding exactly where current price action stands within historical contexts. Welcome to share your opinions! Looking forward to sharing the next tool soon! █ Introduction and How it is Different StatPivot is an advanced technical analysis tool that revolutionizes retracement analysis. Unlike traditional pivot indicators that only show static support/resistance levels, StatPivot delivers dynamic statistical insights based on historical pivot patterns. Its key innovation is real-time percentile calculation - while conventional tools require new pivot formations before updating (often too late for trading decisions), StatPivot continuously analyzes where current price stands within historical retracement distributions. Furthermore, StatPivot provides comprehensive statistical metrics including mean, median, standard deviation, and percentile distributions of price movements, giving traders a probabilistic edge by revealing which price levels represent statistically significant zones for potential reversals or continuations. By transforming raw price data into statistical insights, StatPivot helps traders move beyond subjective price analysis to evidence-based decision making. █ Strategy, How it Works: Detailed Explanation 🔶 Pivot Point Detection and Analysis The core of StatPivot's functionality begins with identifying significant pivot points in the price structure. Using the parameters left and right, the indicator locates pivot highs and lows by examining a specified number of bars to the left and right of each potential pivot point: Copyp_low = ta.pivotlow(low, left, right) p_high = ta.pivothigh(high, left, right) For a point to qualify as a pivot low, it must have left higher lows to its left and right higher lows to its right. Similarly, a pivot high must have left lower highs to its left and right lower highs to its right. This approach ensures that only significant turning points are recognized. 🔶 Percentage Change Calculation Once pivot points are identified, StatPivot calculates the percentage changes between consecutive pivot points: For drops (when a pivot low is lower than the previous pivot low): CopydropPercent = (previous_pivot_low - current_pivot_low) / previous_pivot_low * 100 For rises (when a pivot high is higher than the previous pivot high): CopyrisePercent = (current_pivot_high - previous_pivot_high) / previous_pivot_high * 100 These calculations quantify the magnitude of each market swing, allowing for statistical analysis of historical price movements. 🔶 Statistical Distribution Analysis StatPivot computes comprehensive statistics on the historical distribution of drops and rises: Average (Mean): The arithmetic mean of all recorded percentage changes CopyavgDrop = array.avg(dropValues) Median: The middle value when all percentage changes are arranged in order CopymedianDrop = array.median(dropValues) Standard Deviation: Measures the dispersion of percentage changes from the average CopystdDevDrop = array.stdev(dropValues) Percentiles (25th, 75th): Values below which 25% and 75% of observations fall Copyq1 = array.get(sorted, math.floor(cnt * 0.25)) q3 = array.get(sorted, math.floor(cnt * 0.75)) VaR95: The maximum expected percentage drop with 95% confidence Copyvar95D = array.get(sortedD, math.floor(nD * 0.95)) Coefficient of Variation (CV): Measures relative variability CopycvD = stdDevDrop / avgDrop These statistics provide a comprehensive view of market behavior, enabling traders to understand the typical ranges and extreme moves. 🔶 Real-time Percentile Ranking StatPivot's most innovative feature is its real-time percentile calculation. For each current price, it calculates: The percentage drop from the latest pivot high: CopycurrentDropPct = (latestPivotHigh - close) / latestPivotHigh * 100 The percentage rise from the latest pivot low: CopycurrentRisePct = (close - latestPivotLow) / latestPivotLow * 100 The percentile ranks of these values within the historical distribution: CopyrealtimeDropRank = (count of historical drops <= currentDropPct) / total drops * 100 This calculation reveals exactly where the current price movement stands in relation to all historical movements, providing crucial context for decision-making. 🔶 Cluster Analysis To identify the most common retracement zones, StatPivot performs a cluster analysis by dividing the range of historical drops into five equal intervals: CopyrangeSize = maxVal - minVal For each interval boundary: Copyboundaries = minVal + rangeSize * i / 5 By counting the number of observations in each interval, the indicator identifies the most frequently occurring retracement zones, which often serve as significant support or resistance areas. 🔶 Expected Price Targets Using the statistical data, StatPivot calculates expected price targets: CopytargetBuyPrice = close * (1 - avgDrop / 100) targetSellPrice = close * (1 + avgRise / 100) These targets represent statistically probable price levels for potential entries and exits based on the average historical behavior of the market. █ Trade Direction StatPivot functions as an analytical tool rather than a direct trading signal generator, providing statistical insights that can be applied to various trading strategies. However, the data it generates can be interpreted for different trade directions: For Long Trades: Entry considerations: Look for price drops that reach the 70-80th percentile range in the historical distribution, suggesting a statistically significant retracement Target setting: Use the Expected Sell price or consider the average rise percentage as a reasonable target Risk management: Set stop losses below recent pivot lows or at a distance related to the statistical volatility (standard deviation) For Short Trades: Entry considerations: Look for price rises that reach the 70-80th percentile range, indicating an unusual extension Target setting: Use the Expected Buy price or average drop percentage as a target Risk management: Set stop losses above recent pivot highs or based on statistical measures of volatility For Range Trading: Use the most common drop and rise clusters to identify probable reversal zones Trade bounces between these statistically significant levels For Trend Following: Confirm trend strength by analyzing consecutive higher pivot lows (uptrend) or lower pivot highs (downtrend) Use lower percentile retracements (20-30th percentile) as entry opportunities in established trends █ Usage StatPivot offers multiple ways to integrate its statistical insights into your trading workflow: Statistical Table Analysis: Review the comprehensive statistics displayed in the data table to understand the market's behavior. Pay particular attention to: Average drop and rise percentages to set reasonable expectations Standard deviation to gauge volatility VaR95 for risk assessment Real-time Percentile Monitoring: Watch the real-time percentile display to see where the current price movement stands within the historical distribution. This can help identify: Extreme movements (90th+ percentile) that might indicate reversal opportunities Typical retracements (40-60th percentile) that might continue further Shallow pullbacks (10-30th percentile) that might represent continuation opportunities in trends Support and Resistance Identification: Utilize the plotted pivot points as key support and resistance levels, especially when they align with statistically significant percentile ranges. Target Price Setting: Use the expected buy and sell prices calculated from historical averages as initial targets for your trades. Risk Management: Apply the statistical measurements like standard deviation and VaR95 to set appropriate stop loss levels that account for the market's historical volatility. Pattern Recognition: Over time, learn to recognize when certain percentile levels consistently lead to reversals or continuations in your specific market, and develop personalized strategies based on these observations. █ Default Settings The default settings of StatPivot have been carefully calibrated to provide reliable statistical analysis across a variety of markets and timeframes, but understanding their effects allows for optimal customization: Left Bars (30) and Right Bars (30): These parameters determine how pivot points are identified. With both set to 30 by default: A pivot low must be the lowest point among 30 bars to its left and 30 bars to its right A pivot high must be the highest point among 30 bars to its left and 30 bars to its right Effect on performance: Larger values create fewer but more significant pivot points, reducing noise but potentially missing important market structures. Smaller values generate more pivot points, capturing more nuanced movements but potentially including noise. Table Position (Top Right): Determines where the statistical data table appears on the chart. Effect on performance: No impact on analytical performance, purely a visual preference. Show Distribution Histogram (False): Controls whether the distribution histogram of drop percentages is displayed. Effect on performance: Enabling this provides visual insight into the distribution of retracements but can clutter the chart. Show Real-time Percentile (True): Toggles the display of real-time percentile rankings. Effect on performance: A critical setting that enables the dynamic analysis of current price movements. Disabling this removes one of the key advantages of the indicator. Real-time Percentile Display Mode (Label): Chooses between label display or indicator line for percentile rankings. Effect on performance: Labels provide precise information at the current price point, while indicator lines show the evolution of percentile rankings over time. Advanced Considerations for Settings Optimization: Timeframe Adjustment: Higher timeframes generally benefit from larger Left/Right values to identify truly significant pivots, while lower timeframes may require smaller values to capture shorter-term swings. Volatility-Based Tuning: In highly volatile markets, consider increasing the Left/Right values to filter out noise. In less volatile conditions, lower values can help identify more potential entry and exit points. Market-Specific Optimization: Different markets (forex, stocks, commodities) display different retracement patterns. Monitor the statistics table to see if your market typically shows larger or smaller retracements than the current settings are optimized for. Trading Style Alignment: Adjust the settings to match your trading timeframe. Day traders might prefer settings that identify shorter-term pivots (smaller Left/Right values), while swing traders benefit from more significant pivots (larger Left/Right values). By understanding how these settings affect the analysis and customizing them to your specific market and trading style, you can maximize the effectiveness of StatPivot as a powerful statistical tool for identifying high-probability trading opportunities.
Wskaźnik Pine Script®
od PresentTrading
1
ADX Trend Strength Analyzer█  OVERVIEW This script implements the Average Directional Index (ADX), a powerful tool used to measure the strength of market trends. It works alongside the Directional Movement Index (DMI), which breaks down the directional market pressure into bullish (+DI) and bearish (-DI) components. The purpose of the ADX is to indicate when the market is in a strong trend, without specifying the direction. This indicator can be especially useful for identifying market trends early and validating trading strategies based on trend-following systems. The ADX component in this script is based on two key parameters: ADX Smoothing Length (adxlen), which determines the degree of smoothing for the trend strength. DI Length (dilen), which defines the look-back period for calculating the directional index values. Additionally, a horizontal line is plotted at the 30 level, providing a widely used threshold that signifies when a trend is considered strong (above 30). █  CONCEPTS Directional Movement (DM): The core idea behind this indicator is the calculation of price movement in terms of bullish and bearish forces. By evaluating the change in highs and lows, the script distinguishes between bullish movement (+DM) and bearish movement (-DM). These values are normalized by dividing them by the True Range (TR), creating the +DI and -DI values. True Range (TR): The True Range is calculated using the Average True Range (ATR) formula, and it serves to smooth out volatility, ensuring that short-term fluctuations don't distort the long-term trend signal. ADX Calculation: The ADX is derived from the absolute difference between the +DI and -DI. By smoothing this difference and normalizing it, the ADX is able to measure the overall strength of the trend without regard to whether the market is moving up or down. A rising ADX indicates increasing trend strength, while a falling ADX signals weakening trends. █  METHODOLOGY Directional Movement Calculation: The script first determines the upward and downward price movement by comparing changes in the high and low prices. If the upward movement is greater than the downward movement, it registers a bullish signal and vice versa for bearish movement. True Range Adjustment: The script then applies a smoothing function to normalize these movements by dividing them by the True Range (ATR). This ensures that the trend signal is based on relative, rather than absolute, price movements. ADX Signal Generation: The final step is to calculate the ADX by applying the Relative Moving Average (RMA) to the difference between +DI and -DI. This produces the ADX value, which is plotted in red, making it easy to visualize shifts in market momentum. Threshold Line: A blue horizontal line is plotted at 30, which serves as a key reference point. When the ADX is above this line, it indicates a strong trend, whether bullish or bearish. █  HOW TO USE Trend Strength: Traders typically use the 30 level as a critical threshold. When the ADX is above 30, it signifies a strong trend, making it a favorable environment for trend-following strategies. Conversely, ADX values below 30 suggest a weak or non-trending market. +DI and -DI Relationship: The indicator also provides insight into whether the trend is bullish or bearish. When +DI is greater than -DI, the market is considered bullish. When -DI is greater than +DI, the market is considered bearish. While this script focuses on the ADX value itself, the underlying +DI and -DI help interpret the trend direction. Market Conditions: This indicator is effective in trending markets, but not ideal for choppy or sideways conditions. Traders can use it to determine the best entry and exit points when trends are strong, or to avoid trading in periods of low volatility. Combining with Other Indicators: The ADX is commonly used in conjunction with oscillators like RSI or moving averages, to confirm the trend strength and avoid false signals. █  METHOD VARIANTS This script applies the standard approach for calculating the ADX, but could be adapted with the following variants: Different Timeframes: The script could be modified to calculate ADX values across higher or lower timeframes, depending on the trader's strategy. Custom Thresholds: Instead of using the default 30 threshold, traders could adjust the horizontal line to suit their own risk tolerance or market conditions.
Wskaźnik Pine Script®
od giovannibeltra
Machine Learning: Optimal RSI [YinYangAlgorithms]This Indicator, will rate multiple different lengths of RSIs to determine which RSI to RSI MA cross produced the highest profit within the lookback span. This ‘Optimal RSI’ is then passed back, and if toggled will then be thrown into a Machine Learning calculation. You have the option to Filter RSI and RSI MA’s within the Machine Learning calculation. What this does is, only other Optimal RSI’s which are in the same bullish or bearish direction (is the RSI above or below the RSI MA) will be added to the calculation. You can either (by default) use a Simple Average; which is essentially just a Mean of all the Optimal RSI’s with a length of Machine Learning. Or, you can opt to use a k-Nearest Neighbour (KNN) calculation which takes a Fast and Slow Speed. We essentially turn the Optimal RSI into a MA with different lengths and then compare the distance between the two within our KNN Function. RSI may very well be one of the most used Indicators for identifying crucial Overbought and Oversold locations. Not only that but when it crosses its Moving Average (MA) line it may also indicate good locations to Buy and Sell. Many traders simply use the RSI with the standard length (14), however, does that mean this is the best length? By using the length of the top performing RSI and then applying some Machine Learning logic to it, we hope to create what may be a more accurate, smooth, optimal, RSI. Tutorial: This is a pretty zoomed out Perspective of what the Indicator looks like with its default settings (except with Bollinger Bands and Signals disabled). If you look at the Tables above, you’ll notice, currently the Top Performing RSI Length is 13 with an Optimal Profit % of: 1.00054973. On its default settings, what it does is Scan X amount of RSI Lengths and checks for when the RSI and RSI MA cross each other. It then records the profitability of each cross to identify which length produced the overall highest crossing profitability. Whichever length produces the highest profit is then the RSI length that is used in the plots, until another length takes its place. This may result in what we deem to be the ‘Optimal RSI’ as it is an adaptive RSI which changes based on performance. In our next example, we changed the ‘Optimal RSI Type’ from ‘All Crossings’ to ‘Extremity Crossings’. If you compare the last two examples to each other, you’ll notice some similarities, but overall they’re quite different. The reason why is, the Optimal RSI is calculated differently. When using ‘All Crossings’ everytime the RSI and RSI MA cross, we evaluate it for profit (short and long). However, with ‘Extremity Crossings’, we only evaluate it when the RSI crosses over the RSI MA and RSI <= 40 or RSI crosses under the RSI MA and RSI >= 60. We conclude the crossing when it crosses back on its opposite of the extremity, and that is how it finds its Optimal RSI. The way we determine the Optimal RSI is crucial to calculating which length is currently optimal. In this next example we have zoomed in a bit, and have the full default settings on. Now we have signals (which you can set alerts for), for when the RSI and RSI MA cross (green is bullish and red is bearish). We also have our Optimal RSI Bollinger Bands enabled here too. These bands allow you to see where there may be Support and Resistance within the RSI at levels that aren’t static; such as 30 and 70. The length the RSI Bollinger Bands use is the Optimal RSI Length, allowing it to likewise change in correlation to the Optimal RSI. In the example above, we’ve zoomed out as far as the Optimal RSI Bollinger Bands go. You’ll notice, the Bollinger Bands may act as Support and Resistance locations within and outside of the RSI Mid zone (30-70). In the next example we will highlight these areas so they may be easier to see. Circled above, you may see how many times the Optimal RSI faced Support and Resistance locations on the Bollinger Bands. These Bollinger Bands may give a second location for Support and Resistance. The key Support and Resistance may still be the 30/50/70, however the Bollinger Bands allows us to have a more adaptive, moving form of Support and Resistance. This helps to show where it may ‘bounce’ if it surpasses any of the static levels (30/50/70). Due to the fact that this Indicator may take a long time to execute and it can throw errors for such, we have added a Setting called: Adjust Optimal RSI Lookback and RSI Count. This settings will automatically modify the Optimal RSI Lookback Length and the RSI Count based on the Time Frame you are on and the Bar Indexes that are within. For instance, if we switch to the 1 Hour Time Frame, it will adjust the length from 200->90 and RSI Count from 30->20. If this wasn’t adjusted, the Indicator would Timeout. You may however, change the Setting ‘Adjust Optimal RSI Lookback and RSI Count’ to ‘Manual’ from ‘Auto’. This will give you control over the ‘Optimal RSI Lookback Length’ and ‘RSI Count’ within the Settings. Please note, it will likely take some “fine tuning” to find working settings without the Indicator timing out, but there are definitely times you can find better settings than our ‘Auto’ will create; especially on higher Time Frames. The Minimum our ‘Auto’ will create is: Optimal RSI Lookback Length: 90 RSI Count: 20 The Maximum it will create is: Optimal RSI Lookback Length: 200 RSI Count: 30 If there isn’t much bar index history, for instance, if you’re on the 1 Day and the pair is BTC/USDT you’ll get < 4000 Bar Indexes worth of data. For this reason it is possible to manually increase the settings to say: Optimal RSI Lookback Length: 500 RSI Count: 50 But, please note, if you make it too high, it may also lead to inaccuracies. We will conclude our Tutorial here, hopefully this has given you some insight as to how calculating our Optimal RSI and then using it within Machine Learning may create a more adaptive RSI. Settings: Optimal RSI: Show Crossing Signals: Display signals where the RSI and RSI Cross. Show Tables: Display Information Tables to show information like, Optimal RSI Length, Best Profit, New Optimal RSI Lookback Length and New RSI Count. Show Bollinger Bands: Show RSI Bollinger Bands. These bands work like the TDI Indicator, except its length changes as it uses the current RSI Optimal Length. Optimal RSI Type: This is how we calculate our Optimal RSI. Do we use all RSI and RSI MA Crossings or just when it crosses within the Extremities. Adjust Optimal RSI Lookback and RSI Count: Auto means the script will automatically adjust the Optimal RSI Lookback Length and RSI Count based on the current Time Frame and Bar Index's on chart. This will attempt to stop the script from 'Taking too long to Execute'. Manual means you have full control of the Optimal RSI Lookback Length and RSI Count. Optimal RSI Lookback Length: How far back are we looking to see which RSI length is optimal? Please note the more bars the lower this needs to be. For instance with BTC/USDT you can use 500 here on 1D but only 200 for 15 Minutes; otherwise it will timeout. RSI Count: How many lengths are we checking? For instance, if our 'RSI Minimum Length' is 4 and this is 30, the valid RSI lengths we check is 4-34. RSI Minimum Length: What is the RSI length we start our scans at? We are capped with RSI Count otherwise it will cause the Indicator to timeout, so we don't want to waste any processing power on irrelevant lengths. RSI MA Length: What length are we using to calculate the optimal RSI cross' and likewise plot our RSI MA with? Extremity Crossings RSI Backup Length: When there is no Optimal RSI (if using Extremity Crossings), which RSI should we use instead? Machine Learning: Use Rational Quadratics: Rationalizing our Close may be beneficial for usage within ML calculations. Filter RSI and RSI MA: Should we filter the RSI's before usage in ML calculations? Essentially should we only use RSI data that are of the same type as our Optimal RSI? For instance if our Optimal RSI is Bullish (RSI > RSI MA), should we only use ML RSI's that are likewise bullish? Machine Learning Type: Are we using a Simple ML Average, KNN Mean Average, KNN Exponential Average or None? KNN Distance Type: We need to check if distance is within the KNN Min/Max distance, which distance checks are we using. Machine Learning Length: How far back is our Machine Learning going to keep data for. k-Nearest Neighbour (KNN) Length: How many k-Nearest Neighbours will we account for? Fast ML Data Length: What is our Fast ML Length? This is used with our Slow Length to create our KNN Distance. Slow ML Data Length: What is our Slow ML Length? This is used with our Fast Length to create our KNN Distance. If you have any questions, comments, ideas or concerns please don't hesitate to contact us. HAPPY TRADING!
Wskaźnik Pine Script®
od YinYangAlgorithms
24
vol_rangesThis script shows three measures of volatility: historical (hv): realized volatility of the recent past median (mv): a long run average of realized volatility implied (iv): a user-defined volatility Historical and median volatility are based on the EWMA, rather than standard deviation, method of calculating volatility. Since Tradingview's built in ema function uses a window, the "window" parameter determines how much historical data is used to calculate these volatility measures. E.g. 30 on a daily chart means the previous 30 days. The plots above and below historical candles show past projections based on these measures. The "periods to expiration" dictates how far the projection extends. At 30 periods to expiration (default), the plot will indicate the one standard deviation range from 30 periods ago. This is calculated by multiplying the volatility measure by the square root of time. For example, if the historical volatility (hv) was 20% and the window is 30, then the plot is drawn over: close * 1.2 * sqrt(30/252). At the most recent candle, this same calculation is simply drawn as a line projecting into the future. This script is intended to be used with a particular options contract in mind. For example, if the option expires in 15 days and has an implied volatility of 25%, choose 15 for the window and 25 for the implied volatility options. The ranges drawn will reflect the two standard deviation range both in the future (lines) and at any point in the past (plots) for HV (blue), MV (red), and IV (grey).
Wskaźnik Pine Script®
od taylor_o
1
Volume Profile [Makit0]VOLUME PROFILE INDICATOR v0.5 beta Volume Profile is suitable for day and swing trading on stock and futures markets, is a volume based indicator that gives you 6 key values for each session: POC, VAH, VAL, profile HIGH, LOW and MID levels. This project was born on the idea of plotting the RTH sessions Value Areas for /ES in an automated way, but you can select between 3 different sessions: RTH, GLOBEX and FULL sessions. Some basic concepts: - Volume Profile calculates the total volume for the session at each price level and give us market generated information about what price and range of prices are the most traded (where the value is) - Value Area (VA): range of prices where 70% of the session volume is traded - Value Area High (VAH): highest price within VA - Value Area Low (VAL): lowest price within VA - Point of Control (POC): the most traded price of the session (with the most volume) - Session HIGH, LOW and MID levels are also important There are a huge amount of things to know of Market Profile and Auction Theory like types of days, types of openings, relationships between value areas and openings... for those interested Jim Dalton's work is the way to come I'm in my 2nd trading year and my goal for this year is learning to daytrade the futures markets thru the lens of Market Profile For info on Volume Profile: TV Volume Profile wiki page at www.tradingview.com For info on Market Profile and Market Auction Theory: Jim Dalton's book Mind over markets (this is a MUST) BE AWARE: this indicator is based on the current chart's time interval and it only plots on 1, 2, 3, 5, 10, 15 and 30 minutes charts. This is the correlation table TV uses in the Volume Profile Session Volume indicator (from the wiki above) Chart Indicator 1 - 5 1 6 - 15 5 16 - 30 10 31 - 60 15 61 - 120 30 121 - 1D 60 This indicator doesn't follow that correlation, it doesn't get the volume data from a lower timeframe, it gets the data from the current chart resolution. FEATURES - 6 key values for each session: POC (solid yellow), VAH (solid red), VAL (solid green), profile HIGH (dashed silver), LOW (dashed silver) and MID (dotted silver) levels - 3 sessions to choose for: RTH, GLOBEX and FULL - select the numbers of sessions to plot by adding 12 hours periods back in time - show/hide POC - show/hide VAH & VAL - show/hide session HIGH, LOW & MID levels - highlight the periods of time out of the session (silver) - extend the plotted lines all the way to the right, be careful this can turn the chart unreadable if there are a lot of sessions and lines plotted SETTINGS - Session: select between RTH (8:30 to 15:15 CT), GLOBEX (17:00 to 8:30 CT) and FULL (17:00 to 15:15 CT) sessions. RTH by default - Last 12 hour periods to show: select the deph of the study by adding periods, for example, 60 periods are 30 natural days and around 22 trading days. 1 period by default - Show POC (Point of Control): show/hide POC line. true by default - Show VA (Value Area High & Low): show/hide VAH & VAL lines. true by default - Show Range (Session High, Low & Mid): show/hide session HIGH, LOW & MID lines. true by default - Highlight out of session: show/hide a silver shadow over the non session periods. true by default - Extension: Extend all the plotted lines to the right. false by default HOW TO SETUP BE AWARE THIS INDICATOR PLOTS ONLY IN THE FOLLOWING CHART RESOLUTIONS: 1, 2, 3, 5, 10, 15 AND 30 MINUTES CHARTS. YOU MUST SELECT ONE OF THIS RESOLUTIONS TO THE INDICATOR BE ABLE TO PLOT - By default this indicator plots all the levels for the last RTH session within the last 12 hours, if there is no plot try to adjust the 12 hours periods until the seesion and the periods match - For Globex/Full sessions just select what you want from the dropdown menu and adjust the periods to plot the values - Show or hide the levels you want with the 3 groups: POC line, VA lines and Session Range lines - The highlight and extension options are for a better visibility of the levels as POC or VAH/VAL THANKS TO @watsonexchange for all the help, ideas and insights on this and the last two indicators (Market Delta & Market Internals) I'm working on my way to a 'clean chart' but for me it's not an easy path @PineCoders for all the amazing stuff they do and all the help and tools they provide, in special the Script-Stopwatch at that was key in lowering this indicator's execution time All the TV and Pine community, open source and shared knowledge are indeed the best way to help each other IF YOU REALLY LIKE THIS WORK, please send me a comment or a private message and TELL ME WHAT you trade, HOW you trade it and your FAVOURITE SETUP for pulling out money from the market in a consistent basis, I'm learning to trade (this is my 2nd year) and I need all the help I can get GOOD LUCK AND HAPPY TRADING
Wskaźnik Pine Script®
od makit0
64
BTC/USD 3分バイナリー予測【完全修正版v7.2】## BTC/USD 3-Minute Binary Prediction Indicator v7.2 Documentation ### Overview This indicator predicts whether BTCUSD price will move ±$25 or more within 3 minutes using a 5-layer filter system and pattern recognition. Designed for 30-second timeframe analysis with statistical tracking. ### ⚠️ Critical Warnings - **This is a prediction tool, NOT a profit guarantee** - **30-second timeframe ONLY** (Will not function correctly on other timeframes) - **BTCUSD pair ONLY** - **Past performance does not guarantee future results** - **Test thoroughly in demo before live trading** - **You can lose your entire investment** ### Core Components #### 1. Five-Layer Filter System ``` Layer 1: Trend Alignment (25 points) - EMA configuration analysis Layer 2: Indicator Confluence (25 points) - RSI + MACD confirmation Layer 3: Volume Analysis (20 points) - Abnormal volume detection Layer 4: Key Levels (15 points) - Support/Resistance proximity Layer 5: Momentum (15 points) - Price acceleration measurement ``` #### 2. Pattern Recognition Engine - Double Top/Bottom formations - Triangle patterns (Symmetrical, Ascending, Descending) - Channel patterns - Candlestick patterns (Engulfing, Hammer, Hanging Man) #### 3. Performance Tracking - Overall win rate - Hourly performance - Daily performance - Real-time statistics ### Input Parameters #### Basic Settings - `Range Width ($)`: Target price movement (Default: $50 = ±$25) - `Minimum Confidence (%)`: Required confidence for entry (Default: 75%) - `Max Daily Trades`: Overtrading prevention (Default: 5) #### Filter Settings - `Trend Filter`: EMA alignment validation - `Volume Confirmation`: Volume spike detection - `S/R Filter`: Key level analysis - `Momentum Alignment`: Directional strength confirmation #### Display Settings - `Minimal Mode`: Reduced visual elements for experienced users - `Pattern Display`: Visual pattern overlays - `EMA Lines`: Moving average visualization ### Usage Instructions #### Setup Process 1. Open BTCUSD chart on TradingView 2. **Set timeframe to 30 seconds** (CRITICAL) 3. Add indicator to chart 4. Adjust parameters if needed #### Reading Signals **Decision Panel (Left Side)** - `HIGH`: Predicts +$25 move in 3 minutes - `LOW`: Predicts -$25 move in 3 minutes - `WAIT`: Conditions building, no entry yet - `STAY`: No trade opportunity **F-Score Interpretation** - 65+ points: Direction suggestion (light color) - 80+ points: Strong signal (solid color) - Entry ready: Exclamation mark (!) **Statistics Panel (Right Side)** - Win rate: Percentage of successful predictions - 1H Stats: Last hour performance - Daily Stats: Today's results #### Entry Execution 1. Wait for `HIGH!` or `LOW!` in decision panel 2. Large arrow appears on chart (▲ for HIGH, ▼ for LOW) 3. Enter at **next candle open** 4. Result evaluated after 3 minutes (6 candles) ### Algorithm Logic #### Early Entry System (v7.2 Feature) The indicator uses predictive analysis starting at F-Score 65 instead of waiting for 80: - Faster signal generation - Reduced opportunity loss - Slightly lower win rate trade-off #### Confidence Calculation ``` Base confidence: 50% + Filter score bonus + Pattern recognition bonus + Volatility adjustment - Risk factors = Final confidence (50-95%) ``` ### Technical Specifications #### Multi-Timeframe Analysis - Primary: 30-second chart - Secondary: 1-minute data - Reference: 5-minute trend #### Data Processing - Real-time tick analysis - No repainting (signals are final) - Historical verification system ### Limitations & Known Issues 1. **Spread/Commission Not Included**: Actual profits will be lower 2. **30-Second Noise**: High false signal risk on ultra-short timeframe 3. **Network Latency**: Execution delays not accounted for 4. **Market Conditions**: Performance varies with volatility 5. **Algorithm Trading**: Cannot compete with HFT systems ### Performance Metrics - Target Win Rate: 83% (aspirational) - Evaluation Period: 3 minutes - Risk per trade: Fixed ±$25 - Maximum daily exposure: 5 trades ### Troubleshooting Guide **No signals appearing?** - Check F-Score (needs 65+) - Verify 30-second timeframe - Ensure BTCUSD pair selected **Low win rate?** - Only trade 75%+ confidence signals - Respect daily trade limit - Avoid news events **Pattern shows "-"?** - Normal when no clear pattern exists - Not all market conditions produce patterns ### Code Modification Notes Written in Pine Script v6. Key considerations for modifications: - Global variables use `var` for persistence - Drawing objects require manual management - Bar counting varies by timeframe - Pattern detection runs on confirmed bars only ### Risk Disclosure **IMPORTANT**: Trading cryptocurrencies involves substantial risk of loss and is not suitable for all investors. The high degree of leverage can work against you as well as for you. Before deciding to trade, you should carefully consider your investment objectives, level of experience, and risk appetite. The possibility exists that you could sustain a loss of some or all of your initial investment and therefore you should not invest money that you cannot afford to lose. ### Support & Updates - Report issues in TradingView comments - Check for updates regularly - Version 7.2 is current release - Community feedback welcome ### Legal Notice This indicator is provided for educational and research purposes only. It should not be construed as investment advice. The authors and distributors of this indicator accept no liability for losses incurred through its use. Always consult with a qualified financial advisor before making investment decisions. --- *Version: 7.2* *License: Mozilla Public License 2.0* *Author: * *Last Updated: 2024* **FINAL WARNING: This indicator cannot predict the future. Use at your own risk. Start with demo trading. Never trade with money you cannot afford to lose.**
Wskaźnik Pine Script®
od Hunk77
1
RTH Levels: VWAP + PDH/PDL + ONH/ONL + IBAlgo Index — Levels Pro (ONH/ONL • PDH/PDL • VWAP±Bands • IB • Gaps) Purpose. A session-aware, non-repainting levels tool for intraday decision-making. Designed for futures and indices, with clean visuals, alerts, and a one-click Minimal Mode for screenshot-ready charts. What it plots • PDH/PDL (RTH-only) – Prior Regular Trading Hours high/low, computed intraday and frozen at the RTH close (no 24h mix-ups, no repainting). • ONH/ONL – Prior Overnight high/low, held throughout RTH. • RTH VWAP with ±σ bands – Volume-weighted variance, reset each RTH. • Initial Balance (IB) – First N minutes of RTH, plus 1.5× / 2.0× extensions after IB completes. • Today’s RTH Open & Prior RTH Close – With gap detection and “gap filled” alert. • Killzone shading – NY Open (09:30–10:30 ET) and Lunch (11:15–13:30 ET). • Values panel (top-right) – Each level with live distance in points & ticks. • Right-edge level tags – With anti-overlap (stagger + vertical jitter). • Price-scale tags – Native trackprice markers that always “stick” to the axis. ⸻ New in v6.4 • Minimal Mode: one click for a clean look (thinner lines, VWAP bands/IB extensions hidden, on-chart right-edge labels off; price-scale tags remain). • Theme presets: Dark Hi-Contrast / Light Minimal / Futures Classic / Muted Dark. • Anti-overlap controls: horizontal staggering, vertical jitter, and baseline offset to keep tags readable even when levels cluster. ⸻ Quick start (2 minutes) 1. Add to chart → keep defaults. 2. Sessions (ET): • RTH Session default: 09:30–16:00 (US equities cash hours). • Overnight Session default: 18:00–09:29. Adjust for your market if you use different “day” hours (e.g., many use 08:20–13:30 ET for COMEX Gold). 3. Theme & Minimal Mode: pick a Theme Preset; enable Minimal Mode for screenshots. 4. Visibility: toggle PD/ON/VWAP/IB/References/Panel to taste. 5. Right-edge labels: turn Show Right-Edge Labels on. If they crowd, tune: • Anti-overlap: min separation (ticks) • Horizontal offset per tag (bars) • Vertical jitter per step (ticks) • Right-edge baseline offset (bars) 6. Alerts: open Add alert → Condition: and pick the events you want. ⸻ How levels are computed (no repainting) • PDH/PDL: Intraday H/L are accumulated only while in RTH and saved at RTH close for “yesterday’s” values. • ONH/ONL: Accumulated across the defined Overnight window and then held during RTH. • RTH VWAP & ±σ: Volume-weighted mean and standard deviation, reset at the RTH open. • IB: First N minutes of RTH (default 60). Extensions (1.5×/2.0×) appear after IB completes. • Gaps: Today’s RTH open vs prior RTH close; “Gap Filled” triggers when price trades back to prior close. ⸻ Practical playbooks (how to trade around the levels) 1) PDH/PDL interactions • Rejection: Price taps PDH/PDL then closes back inside → mean-reversion toward VWAP/IB. • Acceptance: Close/hold beyond PDH/PDL with momentum → continuation to next HTF/IB target. • Alert: PD Touch/Break. 2) ONH/ONL “taken” • Often one ON extreme is taken during RTH. ONH Taken / ONL Taken → check if it’s a clean break or sweep & reclaim. • Sweep + reclaim near VWAP can fuel rotations through the ON range. 3) VWAP ±σ framework • Balanced: First tag of ±1σ often reverts toward VWAP. • Trend: Persistent trade beyond ±1σ + IB break → target ±2σ/±3σ. • Alerts: VWAP Cross and VWAP Reject (cross then immediate fail back). 4) IB breaks • After IB completes, a clean IB break commonly targets 1.5× and sometimes 2.0×. • Quick return inside IB = possible fade back to the opposite IB edge/VWAP. • Alerts: IB Break Up / Down. 5) Gaps • Gap-and-go: Opening drive away from prior close + VWAP support → trend until IB completion. • Gap-fill: Weak open and VWAP overhead/underfoot → trade toward prior close; manage on Gap Filled alert. Pro tip: Stack confluences (e.g., ONL sweep + VWAP reclaim + IB hold) and respect your execution rules (e.g., require a 5-minute close in direction, or your order-flow confirmation). ⸻ Inputs you’ll actually touch • Sessions (ET): Session Timezone, RTH Session, Overnight Session. • Visibility: toggles for PD/ON/VWAP/IB/Ref/Panel. • VWAP bands: set σ multipliers (±1/±2/±3). • IB: duration (minutes) and extension multipliers (1.5× / 2.0×). • Style & Theme: Theme Preset, Main Line Width, Trackprice, Minimal Mode, and anti-overlap controls. ⸻ Alerts included • PD Touch/Break — High ≥ PDH or Low ≤ PDL • ONH Taken / ONL Taken — First in-RTH take of ONH/ONL • VWAP Cross — Close crosses VWAP • VWAP Reject — Cross then immediate fail back • IB Break Up / Down — Break of IB High/Low after IB completes • Gap Filled — Price trades back to prior RTH close Setup: Add alert → Condition: Algo Index — Levels Pro → choose event → message → Notify on app/email. ⸻ Panel guide The top-right panel shows each level plus live distance from last price: LevelValue (Δpoints | Δticks) Coloring: green if level is below current price, red if above. ⸻ Styling & screenshot tips • Use Theme Preset that matches your chart. • For dark charts, “Dark Hi-Contrast” with Main Line Width = 3 works well. • Enable Trackprice for crisp axis tags that always stick to the right edge. • Turn on Minimal Mode for cleaner screenshots (no VWAP bands or IB extensions, on-chart tags off; price-scale tags remain). • If tags crowd, increase min separation (ticks) to 30–60 and horizontal offset to 3–5; add vertical jitter (4–12 ticks) and/or push tags farther right with baseline offset (bars). ⸻ Behavior & limitations • Levels are computed incrementally; tables refresh on the last bar for efficiency. • Right-edge labels are placed at bar_index + offset and do not track extra right-margin scrolling (TradingView limitation). The price-scale tags (from trackprice) do track the axis. • “RTH” is what you define in inputs. If your market uses different day hours, change the session strings so PDH/PDL reflect your definition of “yesterday’s session.” ⸻ FAQ Q: My PDH/PDL don’t match the daily chart. A: By design this uses RTH-only highs/lows, not 24h daily bars. Adjust sessions if you want a different definition. Q: Right-edge tags overlap or don’t sit at the far right. A: Increase min separation / horizontal offset / vertical jitter and/or push tags farther with baseline offset. If you want markers that always hug the axis, rely on Trackprice. Q: Can I change killzones? A: Yes—edit the session strings in settings or request a version with user inputs for custom windows. ⸻ Disclaimer Educational use only. This is not financial advice. Always apply your own risk management and confirmation rules. ⸻ Enjoy it? Please ⭐ the script and share screenshots using Minimal Mode + a Theme Preset that fits your style.
Wskaźnik Pine Script®
od MyAlgoIndex
30
Monthly Expected Move (IV + Realized)What it does Overlays 1-month expected move bands on price using both forward-looking options data and backward-looking realized movement: IV30 band — from your pasted 30-day implied vol (%) Straddle band — from your pasted ATM ~30-DTE call+put total HV band — from Historical Volatility computed on-chart ATR band — from ATR% extrapolated to ~1 trading month Use it to quickly answer: “How much could this stock move in ~1 month?” and “Is the market now pricing more/less movement than we’ve actually been getting?” Inputs (quick) Implied (forward-looking) Use IV30 (%) — paste annualized IV30 from your options platform. Use ATM 30-DTE Straddle — paste Call+Put total (per share) at the ATM strike, ~30 DTE. Realized (backward-looking) HV lookback (days) — default 21 (≈1 trading month). ATR length — default 14. Note: TradingView can’t fetch option data automatically. Paste the IV30 % or the straddle total you read from your broker (use Mark/mid prices). How it’s calculated IV band (±%) = IV30 × √(21/252) (annualized → ~1-month). Straddle band (±%) = (ATM Call + Put) / Spot to that expiry (≈30 DTE). HV band (±%) = stdev(log returns, N) × √252 × √(21/252). ATR band (±%) = (ATR(len)/Close) × √21. All bands are plotted as upper/lower envelopes around price, plus an on-chart readout of each ±% for quick scanning. How to use it (at a glance) IV/Straddle bands wider than HV/ATR → market expects bigger movement than recent actuals (possible catalyst/expansion). All bands narrow → likely a low-mover; look elsewhere if you want action. HV > IV → realized swings exceed current pricing (mean-reversion or vol bleed often follows). Pro tips For ATM straddle: pick the expiry closest to ~30 DTE, use the ATM strike (closest to spot), and add Call Mark + Put Mark (per share). If the exact ATM strike isn’t quoted, average the two neighboring strikes. The simple straddle/spot heuristic can read slightly below the IV-derived 1σ; that’s normal. Keep the chart on daily timeframe—the math assumes trading-day conventions (~252/yr, ~21/mo).
Wskaźnik Pine Script®
od rixman14
NY HIGH LOW BREAKNY HIGH LOW BREAK: A New York Session Breakout Strategy The "NY HIGH LOW BREAK" indicator is a powerful TradingView script designed to identify and capitalize on breakout opportunities during the New York trading session. This strategy focuses on the initial price action of the New York market open, looking for clear breaches of the high or low established within the first 30 minutes. It's particularly suited for intraday traders who seek to capture momentum-driven moves. Strategy Logic The core of the "NY HIGH LOW BREAK" strategy revolves around these key components: New York Session Opening Range Identification: The script first identifies the opening range of the New York session. This is defined by the high and low prices established during the first 30 minutes of the New York trading session (from 7:01 AM GMT-4 to 7:31 AM GMT-4). These crucial levels are then extended forward on the chart as horizontal lines, serving as potential support and resistance zones. Breakout Signal Generation: Long Signal: A buy signal is generated when the price breaks above the high of the New York opening range. Specifically, it looks for a candle whose open and close are both above the highLinePrice, and importantly, the previous candle's open was below and close was above the highLinePrice. This indicates a strong upward momentum confirming the breakout. Short Signal: Conversely, a sell signal is generated when the price breaks below the low of the New York opening range. It looks for a candle whose open and close are both below the lowLinePrice, and the previous candle's open was above and close was below the lowLinePrice. This suggests strong downward momentum confirming the breakdown. Supertrend Filter (Implicit/Future Enhancement): While the supertrend and direction variables are present in the code, they are not actively used in the current signal generation logic. This suggests a potential future enhancement where the Supertrend indicator could be incorporated as a trend filter to confirm breakout directions, adding an extra layer of confluence to the signals. For example, only taking long breakouts when Supertrend indicates an uptrend, and short breakouts when Supertrend indicates a downtrend. Second Candle Confirmation (Possible Future Enhancement): The close_sec_candle function and openSEC, closeSEC variables indicate an attempt to capture the open and close of a "second candle" (30 minutes after the initial New York open). Currently, closeSEC is used in a specific condition for signal_way but not directly in the primary longSignal or shortSignal logic. This also suggests a potential future refinement where the price action of this second candle could be used for further confirmation or specific entry criteria. Time-Based Filtering: Signals are only considered valid within a specific trading window from 8:00 AM GMT-4 to 8:00 AM GMT-4 + 16 * 30 minutes (which is 480 minutes, or 8 hours) on 1-minute and 5-minute timeframes. This ensures that trades are taken during the most active and volatile periods of the New York session, avoiding late-session chop. The script also highlights the New York session and lunch hours using background colors, providing visual context to the trading day. Key Features Automated New York Open Range Detection: The script automatically identifies and plots the high and low of the first 30 minutes of the New York trading session. Clear Breakout Signals: Visually distinct "BUY" and "SELL" labels appear on the chart when a breakout occurs, making it easy to spot trading opportunities. Timeframe Adaptability: While optimized for 1-minute and 5-minute timeframes for signal generation, the opening range lines can be displayed on various timeframes. Customizable Risk-to-Reward (RR): The rr input allows users to define their preferred risk-to-reward ratio for potential trades, although it's not directly implemented in the current signal or trade management logic. This could be used by traders for manual trade management. Visual Session and Lunch Highlights: The script colors the background to clearly delineate the New York trading session and the lunch break, helping traders understand the market context. How to Use Apply the Indicator: Add the "NY HIGH LOW BREAK" indicator to your chart on TradingView. Select a Relevant Timeframe: For optimal signal generation, use 1-minute or 5-minute timeframes. Observe the Opening Range: The green and red lines represent the high and low of the first 30 minutes of the New York session. Look for Breakouts: Wait for price to decisively break above the green line (for a buy) or below the red line (for a sell). Confirm Signals: The "BUY" or "SELL" labels will appear on the chart when the breakout conditions are met within the active trading window. Implement Your Risk Management: Use your preferred risk management techniques, including stop-loss and take-profit levels, in conjunction with the signals generated. The rr input can guide your manual risk-to-reward calculations. Potential Enhancements & Considerations Supertrend Confirmation: Integrating the supertrend variable to filter signals would significantly enhance the strategy's robustness by aligning trades with the prevailing trend. Stop-Loss and Take-Profit Automation: The rr input currently serves as a manual guide. Future versions could integrate automated stop-loss and take-profit placement based on this ratio, potentially using ATR for dynamic sizing. Volume Confirmation: Adding a volume filter to confirm breakouts would ensure that only high-conviction moves are traded. Backtesting and Optimization: Thorough backtesting across various assets and market conditions is crucial to determine the optimal settings and profitability of this strategy. Session Times: The current session times are hardcoded. Making these user-definable inputs would allow for greater flexibility across different time zones and trading preferences. The "NY HIGH LOW BREAK" is a straightforward yet effective strategy for capturing initial New York session momentum. By focusing on clear breakout levels, it aims to provide timely and actionable trading signals for intraday traders.
Wskaźnik Pine Script®
od hassansadeghirashed
Profitable Loser Model [MMT]Profitable Loser Model Overview The Profitable Loser Model is a powerful PineScript v6 indicator designed to enhance your trading by visualizing key price levels, session open zones, Fibonacci retracements, and premium/discount zones. This overlay indicator provides traders with a customizable toolkit to analyze market structure across any timeframe, making it ideal for intraday and swing trading strategies. Features Open Zone Visualization - Plots a box based on the open and close of the first candle in a user-defined timeframe (default: 5-minute). - Customizable box color, projection offset, and label size (Tiny, Small, Normal, Large). - Displays a timeframe label (e.g., "5m Open Zone") for quick reference, toggleable on/off. Session Open Lines - Optionally draws horizontal lines at key session opens (8:30 AM, 9:30 AM, 1:30 PM, Midnight, New York time). - Customize line color, style (Solid, Dashed, Dotted), width, and label size for each session. - Perfect for identifying critical intraday price levels. Premium and Discount Zones - Highlights premium (above midpoint) and discount (below midpoint) zones based on session high/low. - Toggleable with customizable colors and projection offsets. - Helps traders spot overbought/oversold areas for potential mean-reversion trades. Fibonacci Retracement Levels - Plots user-defined Fibonacci levels (default: 0.23, 0.35, 0.5, 0.62, 0.705, 0.79, 0.886, 1, 1.1). - Customizable line style, width, color, and labels (showing percentage and/or price). - Dynamically adjusts based on price movement relative to the open zone. Take Profit (TP) and Stop Loss (SL) Levels - Highlights TP (default: 0.23) and SL (default: 1.1) Fibonacci levels with distinct colors. - Fully customizable to align with your risk-reward strategy. How It Works - Session Detection : Resets daily (or per user-defined timeframe) to capture the first candle's open, high, low, and close. - Open Zone : Draws a box between the open and close, extended forward by the projection offset. - Session Lines : Plots lines at specified session opens with customizable styles and labels. - Fibonacci Retracement : Adjusts levels dynamically based on session high/low and price action. - Premium/Discount Zones : Calculated from the session range midpoint, updated in real-time. Settings - Open Zone : - Timeframe (default: 5m), Calculate Timeframe (default: Daily). - Toggle label, adjust size, box color, and projection offset. - Session Open Lines : - Enable/disable lines for 8:30 AM, 9:30 AM, 1:30 PM, Midnight. - Customize color, style, width, label size, and vertical offset. - Premium/Discount Zones : - Toggle visibility, set colors, and adjust projection offset. - Fibonacci Retracement : - Toggle visibility, set custom levels, line style, width, color, and label options. - Adjust projection offset. - TP/SL : - Set TP/SL Fibonacci levels and colors. Use Cases - Intraday Trading : Use session open lines and open zones to trade key market hours. - Swing Trading : Leverage Fibonacci levels for potential reversal or continuation zones. - Risk Management : Set precise TP/SL levels based on Fibonacci retracements. - Market Structure : Identify overbought/oversold zones with premium/discount areas. Notes - Optimized with `dynamic_requests = true` for efficient real-time data handling. - Visual elements (boxes, lines, labels) are cleaned up at the start of each new session. - Session lines use New York time (`America/New_York`) for alignment with major markets.
Wskaźnik Pine Script®
od isthattyler
15
GeeksDoByte 15m & 30m ORB + Prev Day High/LowCME_MINI:NQ1! How It Works Opening Ranges At 9:30 ET, the script begins tracking the high & low. It uses two fixed sessions: 15 min from 09:30 to 09:45 30 min from 09:30 to 10:00 On the very first bar of each session it initializes the range, then continuously updates the high/low on each new intraday bar. Dashed lines are drawn when the session opens and extended horizontally across subsequent bars. Previous Day’s Levels Independently, it fetches yesterday’s high and low via a daily security call. These historic levels are plotted as simple horizontal lines for daily context. How to Use Breakout Entries A close above the 15 min ORB high can signal an early breakout; a further push above the 30 min ORB high confirms extended momentum. Conversely, breaks below the respective lows can indicate short setups. Support & Resistance Yesterday’s high/low often act as magnet levels. If price is near the previous high when the opening ranges break, you get a confluence zone worth watching. Trade Management Combine the two opening-range levels to tier your stops or scale in. For example, you might place an initial stop below the 15 min low and a wider stop below the 30 min low.
Wskaźnik Pine Script®
od RayenGeeksdoByte
Tensor Market Analysis Engine (TMAE)# Tensor Market Analysis Engine (TMAE) ## Advanced Multi-Dimensional Mathematical Analysis System *Where Quantum Mathematics Meets Market Structure* --- ## 🎓 THEORETICAL FOUNDATION The Tensor Market Analysis Engine represents a revolutionary synthesis of three cutting-edge mathematical frameworks that have never before been combined for comprehensive market analysis. This indicator transcends traditional technical analysis by implementing advanced mathematical concepts from quantum mechanics, information theory, and fractal geometry. ### 🌊 Multi-Dimensional Volatility with Jump Detection **Hawkes Process Implementation:** The TMAE employs a sophisticated Hawkes process approximation for detecting self-exciting market jumps. Unlike traditional volatility measures that treat price movements as independent events, the Hawkes process recognizes that market shocks cluster and exhibit memory effects. **Mathematical Foundation:** ``` Intensity λ(t) = μ + Σ α(t - Tᵢ) ``` Where market jumps at times Tᵢ increase the probability of future jumps through the decay function α, controlled by the Hawkes Decay parameter (0.5-0.99). **Mahalanobis Distance Calculation:** The engine calculates volatility jumps using multi-dimensional Mahalanobis distance across up to 5 volatility dimensions: - **Dimension 1:** Price volatility (standard deviation of returns) - **Dimension 2:** Volume volatility (normalized volume fluctuations) - **Dimension 3:** Range volatility (high-low spread variations) - **Dimension 4:** Correlation volatility (price-volume relationship changes) - **Dimension 5:** Microstructure volatility (intrabar positioning analysis) This creates a volatility state vector that captures market behavior impossible to detect with traditional single-dimensional approaches. ### 📐 Hurst Exponent Regime Detection **Fractal Market Hypothesis Integration:** The TMAE implements advanced Rescaled Range (R/S) analysis to calculate the Hurst exponent in real-time, providing dynamic regime classification: - **H > 0.6:** Trending (persistent) markets - momentum strategies optimal - **H < 0.4:** Mean-reverting (anti-persistent) markets - contrarian strategies optimal - **H ≈ 0.5:** Random walk markets - breakout strategies preferred **Adaptive R/S Analysis:** Unlike static implementations, the TMAE uses adaptive windowing that adjusts to market conditions: ``` H = log(R/S) / log(n) ``` Where R is the range of cumulative deviations and S is the standard deviation over period n. **Dynamic Regime Classification:** The system employs hysteresis to prevent regime flipping, requiring sustained Hurst values before regime changes are confirmed. This prevents false signals during transitional periods. ### 🔄 Transfer Entropy Analysis **Information Flow Quantification:** Transfer entropy measures the directional flow of information between price and volume, revealing lead-lag relationships that indicate future price movements: ``` TE(X→Y) = Σ p(yₜ₊₁, yₜ, xₜ) log ``` **Causality Detection:** - **Volume → Price:** Indicates accumulation/distribution phases - **Price → Volume:** Suggests retail participation or momentum chasing - **Balanced Flow:** Market equilibrium or transition periods The system analyzes multiple lag periods (2-20 bars) to capture both immediate and structural information flows. --- ## 🔧 COMPREHENSIVE INPUT SYSTEM ### Core Parameters Group **Primary Analysis Window (10-100, Default: 50)** The fundamental lookback period affecting all calculations. Optimization by timeframe: - **1-5 minute charts:** 20-30 (rapid adaptation to micro-movements) - **15 minute-1 hour:** 30-50 (balanced responsiveness and stability) - **4 hour-daily:** 50-100 (smooth signals, reduced noise) - **Asset-specific:** Cryptocurrency 20-35, Stocks 35-50, Forex 40-60 **Signal Sensitivity (0.1-2.0, Default: 0.7)** Master control affecting all threshold calculations: - **Conservative (0.3-0.6):** High-quality signals only, fewer false positives - **Balanced (0.7-1.0):** Optimal risk-reward ratio for most trading styles - **Aggressive (1.1-2.0):** Maximum signal frequency, requires careful filtering **Signal Generation Mode:** - **Aggressive:** Any component signals (highest frequency) - **Confluence:** 2+ components agree (balanced approach) - **Conservative:** All 3 components align (highest quality) ### Volatility Jump Detection Group **Volatility Dimensions (2-5, Default: 3)** Determines the mathematical space complexity: - **2D:** Price + Volume volatility (suitable for clean markets) - **3D:** + Range volatility (optimal for most conditions) - **4D:** + Correlation volatility (advanced multi-asset analysis) - **5D:** + Microstructure volatility (maximum sensitivity) **Jump Detection Threshold (1.5-4.0σ, Default: 3.0σ)** Standard deviations required for volatility jump classification: - **Cryptocurrency:** 2.0-2.5σ (naturally volatile) - **Stock Indices:** 2.5-3.0σ (moderate volatility) - **Forex Major Pairs:** 3.0-3.5σ (typically stable) - **Commodities:** 2.0-3.0σ (varies by commodity) **Jump Clustering Decay (0.5-0.99, Default: 0.85)** Hawkes process memory parameter: - **0.5-0.7:** Fast decay (jumps treated as independent) - **0.8-0.9:** Moderate clustering (realistic market behavior) - **0.95-0.99:** Strong clustering (crisis/event-driven markets) ### Hurst Exponent Analysis Group **Calculation Method Options:** - **Classic R/S:** Original Rescaled Range (fast, simple) - **Adaptive R/S:** Dynamic windowing (recommended for trading) - **DFA:** Detrended Fluctuation Analysis (best for noisy data) **Trending Threshold (0.55-0.8, Default: 0.60)** Hurst value defining persistent market behavior: - **0.55-0.60:** Weak trend persistence - **0.65-0.70:** Clear trending behavior - **0.75-0.80:** Strong momentum regimes **Mean Reversion Threshold (0.2-0.45, Default: 0.40)** Hurst value defining anti-persistent behavior: - **0.35-0.45:** Weak mean reversion - **0.25-0.35:** Clear ranging behavior - **0.15-0.25:** Strong reversion tendency ### Transfer Entropy Parameters Group **Information Flow Analysis:** - **Price-Volume:** Classic flow analysis for accumulation/distribution - **Price-Volatility:** Risk flow analysis for sentiment shifts - **Multi-Timeframe:** Cross-timeframe causality detection **Maximum Lag (2-20, Default: 5)** Causality detection window: - **2-5 bars:** Immediate causality (scalping) - **5-10 bars:** Short-term flow (day trading) - **10-20 bars:** Structural flow (swing trading) **Significance Threshold (0.05-0.3, Default: 0.15)** Minimum entropy for signal generation: - **0.05-0.10:** Detect subtle information flows - **0.10-0.20:** Clear causality only - **0.20-0.30:** Very strong flows only --- ## 🎨 ADVANCED VISUAL SYSTEM ### Tensor Volatility Field Visualization **Five-Layer Resonance Bands:** The tensor field creates dynamic support/resistance zones that expand and contract based on mathematical field strength: - **Core Layer (Purple):** Primary tensor field with highest intensity - **Layer 2 (Neutral):** Secondary mathematical resonance - **Layer 3 (Info Blue):** Tertiary harmonic frequencies - **Layer 4 (Warning Gold):** Outer field boundaries - **Layer 5 (Success Green):** Maximum field extension **Field Strength Calculation:** ``` Field Strength = min(3.0, Mahalanobis Distance × Tensor Intensity) ``` The field amplitude adjusts to ATR and mathematical distance, creating dynamic zones that respond to market volatility. **Radiation Line Network:** During active tensor states, the system projects directional radiation lines showing field energy distribution: - **8 Directional Rays:** Complete angular coverage - **Tapering Segments:** Progressive transparency for natural visual flow - **Pulse Effects:** Enhanced visualization during volatility jumps ### Dimensional Portal System **Portal Mathematics:** Dimensional portals visualize regime transitions using category theory principles: - **Green Portals (◉):** Trending regime detection (appear below price for support) - **Red Portals (◎):** Mean-reverting regime (appear above price for resistance) - **Yellow Portals (○):** Random walk regime (neutral positioning) **Tensor Trail Effects:** Each portal generates 8 trailing particles showing mathematical momentum: - **Large Particles (●):** Strong mathematical signal - **Medium Particles (◦):** Moderate signal strength - **Small Particles (·):** Weak signal continuation - **Micro Particles (˙):** Signal dissipation ### Information Flow Streams **Particle Stream Visualization:** Transfer entropy creates flowing particle streams indicating information direction: - **Upward Streams:** Volume leading price (accumulation phases) - **Downward Streams:** Price leading volume (distribution phases) - **Stream Density:** Proportional to information flow strength **15-Particle Evolution:** Each stream contains 15 particles with progressive sizing and transparency, creating natural flow visualization that makes information transfer immediately apparent. ### Fractal Matrix Grid System **Multi-Timeframe Fractal Levels:** The system calculates and displays fractal highs/lows across five Fibonacci periods: - **8-Period:** Short-term fractal structure - **13-Period:** Intermediate-term patterns - **21-Period:** Primary swing levels - **34-Period:** Major structural levels - **55-Period:** Long-term fractal boundaries **Triple-Layer Visualization:** Each fractal level uses three-layer rendering: - **Shadow Layer:** Widest, darkest foundation (width 5) - **Glow Layer:** Medium white core line (width 3) - **Tensor Layer:** Dotted mathematical overlay (width 1) **Intelligent Labeling System:** Smart spacing prevents label overlap using ATR-based minimum distances. Labels include: - **Fractal Period:** Time-based identification - **Topological Class:** Mathematical complexity rating (0, I, II, III) - **Price Level:** Exact fractal price - **Mahalanobis Distance:** Current mathematical field strength - **Hurst Exponent:** Current regime classification - **Anomaly Indicators:** Visual strength representations (○ ◐ ● ⚡) ### Wick Pressure Analysis **Rejection Level Mathematics:** The system analyzes candle wick patterns to project future pressure zones: - **Upper Wick Analysis:** Identifies selling pressure and resistance zones - **Lower Wick Analysis:** Identifies buying pressure and support zones - **Pressure Projection:** Extends lines forward based on mathematical probability **Multi-Layer Glow Effects:** Wick pressure lines use progressive transparency (1-8 layers) creating natural glow effects that make pressure zones immediately visible without cluttering the chart. ### Enhanced Regime Background **Dynamic Intensity Mapping:** Background colors reflect mathematical regime strength: - **Deep Transparency (98% alpha):** Subtle regime indication - **Pulse Intensity:** Based on regime strength calculation - **Color Coding:** Green (trending), Red (mean-reverting), Neutral (random) **Smoothing Integration:** Regime changes incorporate 10-bar smoothing to prevent background flicker while maintaining responsiveness to genuine regime shifts. ### Color Scheme System **Six Professional Themes:** - **Dark (Default):** Professional trading environment optimization - **Light:** High ambient light conditions - **Classic:** Traditional technical analysis appearance - **Neon:** High-contrast visibility for active trading - **Neutral:** Minimal distraction focus - **Bright:** Maximum visibility for complex setups Each theme maintains mathematical accuracy while optimizing visual clarity for different trading environments and personal preferences. --- ## 📊 INSTITUTIONAL-GRADE DASHBOARD ### Tensor Field Status Section **Field Strength Display:** Real-time Mahalanobis distance calculation with dynamic emoji indicators: - **⚡ (Lightning):** Extreme field strength (>1.5× threshold) - **● (Solid Circle):** Strong field activity (>1.0× threshold) - **○ (Open Circle):** Normal field state **Signal Quality Rating:** Democratic algorithm assessment: - **ELITE:** All 3 components aligned (highest probability) - **STRONG:** 2 components aligned (good probability) - **GOOD:** 1 component active (moderate probability) - **WEAK:** No clear component signals **Threshold and Anomaly Monitoring:** - **Threshold Display:** Current mathematical threshold setting - **Anomaly Level (0-100%):** Combined volatility and volume spike measurement - **>70%:** High anomaly (red warning) - **30-70%:** Moderate anomaly (orange caution) - **<30%:** Normal conditions (green confirmation) ### Tensor State Analysis Section **Mathematical State Classification:** - **↑ BULL (Tensor State +1):** Trending regime with bullish bias - **↓ BEAR (Tensor State -1):** Mean-reverting regime with bearish bias - **◈ SUPER (Tensor State 0):** Random walk regime (neutral) **Visual State Gauge:** Five-circle progression showing tensor field polarity: - **🟢🟢🟢⚪⚪:** Strong bullish mathematical alignment - **⚪⚪🟡⚪⚪:** Neutral/transitional state - **⚪⚪🔴🔴🔴:** Strong bearish mathematical alignment **Trend Direction and Phase Analysis:** - **📈 BULL / 📉 BEAR / ➡️ NEUTRAL:** Primary trend classification - **🌪️ CHAOS:** Extreme information flow (>2.0 flow strength) - **⚡ ACTIVE:** Strong information flow (1.0-2.0 flow strength) - **😴 CALM:** Low information flow (<1.0 flow strength) ### Trading Signals Section **Real-Time Signal Status:** - **🟢 ACTIVE / ⚪ INACTIVE:** Long signal availability - **🔴 ACTIVE / ⚪ INACTIVE:** Short signal availability - **Components (X/3):** Active algorithmic components - **Mode Display:** Current signal generation mode **Signal Strength Visualization:** Color-coded component count: - **Green:** 3/3 components (maximum confidence) - **Aqua:** 2/3 components (good confidence) - **Orange:** 1/3 components (moderate confidence) - **Gray:** 0/3 components (no signals) ### Performance Metrics Section **Win Rate Monitoring:** Estimated win rates based on signal quality with emoji indicators: - **🔥 (Fire):** ≥60% estimated win rate - **👍 (Thumbs Up):** 45-59% estimated win rate - **⚠️ (Warning):** <45% estimated win rate **Mathematical Metrics:** - **Hurst Exponent:** Real-time fractal dimension (0.000-1.000) - **Information Flow:** Volume/price leading indicators - **📊 VOL:** Volume leading price (accumulation/distribution) - **💰 PRICE:** Price leading volume (momentum/speculation) - **➖ NONE:** Balanced information flow - **Volatility Classification:** - **🔥 HIGH:** Above 1.5× jump threshold - **📊 NORM:** Normal volatility range - **😴 LOW:** Below 0.5× jump threshold ### Market Structure Section (Large Dashboard) **Regime Classification:** - **📈 TREND:** Hurst >0.6, momentum strategies optimal - **🔄 REVERT:** Hurst <0.4, contrarian strategies optimal - **🎲 RANDOM:** Hurst ≈0.5, breakout strategies preferred **Mathematical Field Analysis:** - **Dimensions:** Current volatility space complexity (2D-5D) - **Hawkes λ (Lambda):** Self-exciting jump intensity (0.00-1.00) - **Jump Status:** 🚨 JUMP (active) / ✅ NORM (normal) ### Settings Summary Section (Large Dashboard) **Active Configuration Display:** - **Sensitivity:** Current master sensitivity setting - **Lookback:** Primary analysis window - **Theme:** Active color scheme - **Method:** Hurst calculation method (Classic R/S, Adaptive R/S, DFA) **Dashboard Sizing Options:** - **Small:** Essential metrics only (mobile/small screens) - **Normal:** Balanced information density (standard desktop) - **Large:** Maximum detail (multi-monitor setups) **Position Options:** - **Top Right:** Standard placement (avoids price action) - **Top Left:** Wide chart optimization - **Bottom Right:** Recent price focus (scalping) - **Bottom Left:** Maximum price visibility (swing trading) --- ## 🎯 SIGNAL GENERATION LOGIC ### Multi-Component Convergence System **Component Signal Architecture:** The TMAE generates signals through sophisticated component analysis rather than simple threshold crossing: **Volatility Component:** - **Jump Detection:** Mahalanobis distance threshold breach - **Hawkes Intensity:** Self-exciting process activation (>0.2) - **Multi-dimensional:** Considers all volatility dimensions simultaneously **Hurst Regime Component:** - **Trending Markets:** Price above SMA-20 with positive momentum - **Mean-Reverting Markets:** Price at Bollinger Band extremes - **Random Markets:** Bollinger squeeze breakouts with directional confirmation **Transfer Entropy Component:** - **Volume Leadership:** Information flow from volume to price - **Volume Spike:** Volume 110%+ above 20-period average - **Flow Significance:** Above entropy threshold with directional bias ### Democratic Signal Weighting **Signal Mode Implementation:** - **Aggressive Mode:** Any single component triggers signal - **Confluence Mode:** Minimum 2 components must agree - **Conservative Mode:** All 3 components must align **Momentum Confirmation:** All signals require momentum confirmation: - **Long Signals:** RSI >50 AND price >EMA-9 - **Short Signals:** RSI <50 AND price 0.6):** - **Increase Sensitivity:** Catch momentum continuation - **Lower Mean Reversion Threshold:** Avoid counter-trend signals - **Emphasize Volume Leadership:** Institutional accumulation/distribution - **Tensor Field Focus:** Use expansion for trend continuation - **Signal Mode:** Aggressive or Confluence for trend following **Range-Bound Markets (Hurst <0.4):** - **Decrease Sensitivity:** Avoid false breakouts - **Lower Trending Threshold:** Quick regime recognition - **Focus on Price Leadership:** Retail sentiment extremes - **Fractal Grid Emphasis:** Support/resistance trading - **Signal Mode:** Conservative for high-probability reversals **Volatile Markets (High Jump Frequency):** - **Increase Hawkes Decay:** Recognize event clustering - **Higher Jump Threshold:** Avoid noise signals - **Maximum Dimensions:** Capture full volatility complexity - **Reduce Position Sizing:** Risk management adaptation - **Enhanced Visuals:** Maximum information for rapid decisions **Low Volatility Markets (Low Jump Frequency):** - **Decrease Jump Threshold:** Capture subtle movements - **Lower Hawkes Decay:** Treat moves as independent - **Reduce Dimensions:** Simplify analysis - **Increase Position Sizing:** Capitalize on compressed volatility - **Minimal Visuals:** Reduce distraction in quiet markets --- ## 🚀 ADVANCED TRADING STRATEGIES ### The Mathematical Convergence Method **Entry Protocol:** 1. **Fractal Grid Approach:** Monitor price approaching significant fractal levels 2. **Tensor Field Confirmation:** Verify field expansion supporting direction 3. **Portal Signal:** Wait for dimensional portal appearance 4. **ELITE/STRONG Quality:** Only trade highest quality mathematical signals 5. **Component Consensus:** Confirm 2+ components agree in Confluence mode **Example Implementation:** - Price approaching 21-period fractal high - Tensor field expanding upward (bullish mathematical alignment) - Green portal appears below price (trending regime confirmation) - ELITE quality signal with 3/3 components active - Enter long position with stop below fractal level **Risk Management:** - **Stop Placement:** Below/above fractal level that generated signal - **Position Sizing:** Based on Mahalanobis distance (higher distance = smaller size) - **Profit Targets:** Next fractal level or tensor field resistance ### The Regime Transition Strategy **Regime Change Detection:** 1. **Monitor Hurst Exponent:** Watch for persistent moves above/below thresholds 2. **Portal Color Change:** Regime transitions show different portal colors 3. **Background Intensity:** Increasing regime background intensity 4. **Mathematical Confirmation:** Wait for regime confirmation (hysteresis) **Trading Implementation:** - **Trending Transitions:** Trade momentum breakouts, follow trend - **Mean Reversion Transitions:** Trade range boundaries, fade extremes - **Random Transitions:** Trade breakouts with tight stops **Advanced Techniques:** - **Multi-Timeframe:** Confirm regime on higher timeframe - **Early Entry:** Enter on regime transition rather than confirmation - **Regime Strength:** Larger positions during strong regime signals ### The Information Flow Momentum Strategy **Flow Detection Protocol:** 1. **Monitor Transfer Entropy:** Watch for significant information flow shifts 2. **Volume Leadership:** Strong edge when volume leads price 3. **Flow Acceleration:** Increasing flow strength indicates momentum 4. **Directional Confirmation:** Ensure flow aligns with intended trade direction **Entry Signals:** - **Volume → Price Flow:** Enter during accumulation/distribution phases - **Price → Volume Flow:** Enter on momentum confirmation breaks - **Flow Reversal:** Counter-trend entries when flow reverses **Optimization:** - **Scalping:** Use immediate flow detection (2-5 bar lag) - **Swing Trading:** Use structural flow (10-20 bar lag) - **Multi-Asset:** Compare flow between correlated assets ### The Tensor Field Expansion Strategy **Field Mathematics:** The tensor field expansion indicates mathematical pressure building in market structure: **Expansion Phases:** 1. **Compression:** Field contracts, volatility decreases 2. **Tension Building:** Mathematical pressure accumulates 3. **Expansion:** Field expands rapidly with directional movement 4. **Resolution:** Field stabilizes at new equilibrium **Trading Applications:** - **Compression Trading:** Prepare for breakout during field contraction - **Expansion Following:** Trade direction of field expansion - **Reversion Trading:** Fade extreme field expansion - **Multi-Dimensional:** Consider all field layers for confirmation ### The Hawkes Process Event Strategy **Self-Exciting Jump Trading:** Understanding that market shocks cluster and create follow-on opportunities: **Jump Sequence Analysis:** 1. **Initial Jump:** First volatility jump detected 2. **Clustering Phase:** Hawkes intensity remains elevated 3. **Follow-On Opportunities:** Additional jumps more likely 4. **Decay Period:** Intensity gradually decreases **Implementation:** - **Jump Confirmation:** Wait for mathematical jump confirmation - **Direction Assessment:** Use other components for direction - **Clustering Trades:** Trade subsequent moves during high intensity - **Decay Exit:** Exit positions as Hawkes intensity decays ### The Fractal Confluence System **Multi-Timeframe Fractal Analysis:** Combining fractal levels across different periods for high-probability zones: **Confluence Zones:** - **Double Confluence:** 2 fractal levels align - **Triple Confluence:** 3+ fractal levels cluster - **Mathematical Confirmation:** Tensor field supports the level - **Information Flow:** Transfer entropy confirms direction **Trading Protocol:** 1. **Identify Confluence:** Find 2+ fractal levels within 1 ATR 2. **Mathematical Support:** Verify tensor field alignment 3. **Signal Quality:** Wait for STRONG or ELITE signal 4. **Risk Definition:** Use fractal level for stop placement 5. **Profit Targeting:** Next major fractal confluence zone --- ## ⚠️ COMPREHENSIVE RISK MANAGEMENT ### Mathematical Position Sizing **Mahalanobis Distance Integration:** Position size should inversely correlate with mathematical field strength: ``` Position Size = Base Size × (Threshold / Mahalanobis Distance) ``` **Risk Scaling Matrix:** - **Low Field Strength (<2.0):** Standard position sizing - **Moderate Field Strength (2.0-3.0):** 75% position sizing - **High Field Strength (3.0-4.0):** 50% position sizing - **Extreme Field Strength (>4.0):** 25% position sizing or no trade ### Signal Quality Risk Adjustment **Quality-Based Position Sizing:** - **ELITE Signals:** 100% of planned position size - **STRONG Signals:** 75% of planned position size - **GOOD Signals:** 50% of planned position size - **WEAK Signals:** No position or paper trading only **Component Agreement Scaling:** - **3/3 Components:** Full position size - **2/3 Components:** 75% position size - **1/3 Components:** 50% position size or skip trade ### Regime-Adaptive Risk Management **Trending Market Risk:** - **Wider Stops:** Allow for trend continuation - **Trend Following:** Trade with regime direction - **Higher Position Size:** Trend probability advantage - **Momentum Stops:** Trail stops based on momentum indicators **Mean-Reverting Market Risk:** - **Tighter Stops:** Quick exits on trend continuation - **Contrarian Positioning:** Trade against extremes - **Smaller Position Size:** Higher reversal failure rate - **Level-Based Stops:** Use fractal levels for stops **Random Market Risk:** - **Breakout Focus:** Trade only clear breakouts - **Tight Initial Stops:** Quick exit if breakout fails - **Reduced Frequency:** Skip marginal setups - **Range-Based Targets:** Profit targets at range boundaries ### Volatility-Adaptive Risk Controls **High Volatility Periods:** - **Reduced Position Size:** Account for wider price swings - **Wider Stops:** Avoid noise-based exits - **Lower Frequency:** Skip marginal setups - **Faster Exits:** Take profits more quickly **Low Volatility Periods:** - **Standard Position Size:** Normal risk parameters - **Tighter Stops:** Take advantage of compressed ranges - **Higher Frequency:** Trade more setups - **Extended Targets:** Allow for compressed volatility expansion ### Multi-Timeframe Risk Alignment **Higher Timeframe Trend:** - **With Trend:** Standard or increased position size - **Against Trend:** Reduced position size or skip - **Neutral Trend:** Standard position size with tight management **Risk Hierarchy:** 1. **Primary:** Current timeframe signal quality 2. **Secondary:** Higher timeframe trend alignment 3. **Tertiary:** Mathematical field strength 4. **Quaternary:** Market regime classification --- ## 📚 EDUCATIONAL VALUE AND MATHEMATICAL CONCEPTS ### Advanced Mathematical Concepts **Tensor Analysis in Markets:** The TMAE introduces traders to tensor analysis, a branch of mathematics typically reserved for physics and advanced engineering. Tensors provide a framework for understanding multi-dimensional market relationships that scalar and vector analysis cannot capture. **Information Theory Applications:** Transfer entropy implementation teaches traders about information flow in markets, a concept from information theory that quantifies directional causality between variables. This provides intuition about market microstructure and participant behavior. **Fractal Geometry in Trading:** The Hurst exponent calculation exposes traders to fractal geometry concepts, helping understand that markets exhibit self-similar patterns across multiple timeframes. This mathematical insight transforms how traders view market structure. **Stochastic Process Theory:** The Hawkes process implementation introduces concepts from stochastic process theory, specifically self-exciting point processes. This provides mathematical framework for understanding why market events cluster and exhibit memory effects. ### Learning Progressive Complexity **Beginner Mathematical Concepts:** - **Volatility Dimensions:** Understanding multi-dimensional analysis - **Regime Classification:** Learning market personality types - **Signal Democracy:** Algorithmic consensus building - **Visual Mathematics:** Interpreting mathematical concepts visually **Intermediate Mathematical Applications:** - **Mahalanobis Distance:** Statistical distance in multi-dimensional space - **Rescaled Range Analysis:** Fractal dimension measurement - **Information Entropy:** Quantifying uncertainty and causality - **Field Theory:** Understanding mathematical fields in market context **Advanced Mathematical Integration:** - **Tensor Field Dynamics:** Multi-dimensional market force analysis - **Stochastic Self-Excitation:** Event clustering and memory effects - **Categorical Composition:** Mathematical signal combination theory - **Topological Market Analysis:** Understanding market shape and connectivity ### Practical Mathematical Intuition **Developing Market Mathematics Intuition:** The TMAE serves as a bridge between abstract mathematical concepts and practical trading applications. Traders develop intuitive understanding of: - **How markets exhibit mathematical structure beneath apparent randomness** - **Why multi-dimensional analysis reveals patterns invisible to single-variable approaches** - **How information flows through markets in measurable, predictable ways** - **Why mathematical models provide probabilistic edges rather than certainties** --- ## 🔬 IMPLEMENTATION AND OPTIMIZATION ### Getting Started Protocol **Phase 1: Observation (Week 1)** 1. **Apply with defaults:** Use standard settings on your primary trading timeframe 2. **Study visual elements:** Learn to interpret tensor fields, portals, and streams 3. **Monitor dashboard:** Observe how metrics change with market conditions 4. **No trading:** Focus entirely on pattern recognition and understanding **Phase 2: Pattern Recognition (Week 2-3)** 1. **Identify signal patterns:** Note what market conditions produce different signal qualities 2. **Regime correlation:** Observe how Hurst regimes affect signal performance 3. **Visual confirmation:** Learn to read tensor field expansion and portal signals 4. **Component analysis:** Understand which components drive signals in different markets **Phase 3: Parameter Optimization (Week 4-5)** 1. **Asset-specific tuning:** Adjust parameters for your specific trading instrument 2. **Timeframe optimization:** Fine-tune for your preferred trading timeframe 3. **Sensitivity adjustment:** Balance signal frequency with quality 4. **Visual customization:** Optimize colors and intensity for your trading environment **Phase 4: Live Implementation (Week 6+)** 1. **Paper trading:** Test signals with hypothetical trades 2. **Small position sizing:** Begin with minimal risk during learning phase 3. **Performance tracking:** Monitor actual vs. expected signal performance 4. **Continuous optimization:** Refine settings based on real performance data ### Performance Monitoring System **Signal Quality Tracking:** - **ELITE Signal Win Rate:** Track highest quality signals separately - **Component Performance:** Monitor which components provide best signals - **Regime Performance:** Analyze performance across different market regimes - **Timeframe Analysis:** Compare performance across different session times **Mathematical Metric Correlation:** - **Field Strength vs. Performance:** Higher field strength should correlate with better performance - **Component Agreement vs. Win Rate:** More component agreement should improve win rates - **Regime Alignment vs. Success:** Trading with mathematical regime should outperform ### Continuous Optimization Process **Monthly Review Protocol:** 1. **Performance Analysis:** Review win rates, profit factors, and maximum drawdown 2. **Parameter Assessment:** Evaluate if current settings remain optimal 3. **Market Adaptation:** Adjust for changes in market character or volatility 4. **Component Weighting:** Consider if certain components should receive more/less emphasis **Quarterly Deep Analysis:** 1. **Mathematical Model Validation:** Verify that mathematical relationships remain valid 2. **Regime Distribution:** Analyze time spent in different market regimes 3. **Signal Evolution:** Track how signal characteristics change over time 4. **Correlation Analysis:** Monitor correlations between different mathematical components --- ## 🌟 UNIQUE INNOVATIONS AND CONTRIBUTIONS ### Revolutionary Mathematical Integration **First-Ever Implementations:** 1. **Multi-Dimensional Volatility Tensor:** First indicator to implement true tensor analysis for market volatility 2. **Real-Time Hawkes Process:** First trading implementation of self-exciting point processes 3. **Transfer Entropy Trading Signals:** First practical application of information theory for trade generation 4. **Democratic Component Voting:** First algorithmic consensus system for signal generation 5. **Fractal-Projected Signal Quality:** First system to predict signal quality at future price levels ### Advanced Visualization Innovations **Mathematical Visualization Breakthroughs:** - **Tensor Field Radiation:** Visual representation of mathematical field energy - **Dimensional Portal System:** Category theory visualization for regime transitions - **Information Flow Streams:** Real-time visual display of market information transfer - **Multi-Layer Fractal Grid:** Intelligent spacing and projection system - **Regime Intensity Mapping:** Dynamic background showing mathematical regime strength ### Practical Trading Innovations **Trading System Advances:** - **Quality-Weighted Signal Generation:** Signals rated by mathematical confidence - **Regime-Adaptive Strategy Selection:** Automatic strategy optimization based on market personality - **Anti-Spam Signal Protection:** Mathematical prevention of signal clustering - **Component Performance Tracking:** Real-time monitoring of algorithmic component success - **Field-Strength Position Sizing:** Mathematical volatility integration for risk management --- ## ⚖️ RESPONSIBLE USAGE AND LIMITATIONS ### Mathematical Model Limitations **Understanding Model Boundaries:** While the TMAE implements sophisticated mathematical concepts, traders must understand fundamental limitations: - **Markets Are Not Purely Mathematical:** Human psychology, news events, and fundamental factors create unpredictable elements - **Past Performance Limitations:** Mathematical relationships that worked historically may not persist indefinitely - **Model Risk:** Complex models can fail during unprecedented market conditions - **Overfitting Potential:** Highly optimized parameters may not generalize to future market conditions ### Proper Implementation Guidelines **Risk Management Requirements:** - **Never Risk More Than 2% Per Trade:** Regardless of signal quality - **Diversification Mandatory:** Don't rely solely on mathematical signals - **Position Sizing Discipline:** Use mathematical field strength for sizing, not confidence - **Stop Loss Non-Negotiable:** Every trade must have predefined risk parameters **Realistic Expectations:** - **Mathematical Edge, Not Certainty:** The indicator provides probabilistic advantages, not guaranteed outcomes - **Learning Curve Required:** Complex mathematical concepts require time to master - **Market Adaptation Necessary:** Parameters must evolve with changing market conditions - **Continuous Education Important:** Understanding underlying mathematics improves application ### Ethical Trading Considerations **Market Impact Awareness:** - **Information Asymmetry:** Advanced mathematical analysis may provide advantages over other market participants - **Position Size Responsibility:** Large positions based on mathematical signals can impact market structure - **Sharing Knowledge:** Consider educational contributions to trading community - **Fair Market Participation:** Use mathematical advantages responsibly within market framework ### Professional Development Path **Skill Development Sequence:** 1. **Basic Mathematical Literacy:** Understand fundamental concepts before advanced application 2. **Risk Management Mastery:** Develop disciplined risk control before relying on complex signals 3. **Market Psychology Understanding:** Combine mathematical analysis with behavioral market insights 4. **Continuous Learning:** Stay updated on mathematical finance developments and market evolution --- ## 🔮 CONCLUSION The Tensor Market Analysis Engine represents a quantum leap forward in technical analysis, successfully bridging the gap between advanced pure mathematics and practical trading applications. By integrating multi-dimensional volatility analysis, fractal market theory, and information flow dynamics, the TMAE reveals market structure invisible to conventional analysis while maintaining visual clarity and practical usability. ### Mathematical Innovation Legacy This indicator establishes new paradigms in technical analysis: - **Tensor analysis for market volatility understanding** - **Stochastic self-excitation for event clustering prediction** - **Information theory for causality-based trade generation** - **Democratic algorithmic consensus for signal quality enhancement** - **Mathematical field visualization for intuitive market understanding** ### Practical Trading Revolution Beyond mathematical innovation, the TMAE transforms practical trading: - **Quality-rated signals replace binary buy/sell decisions** - **Regime-adaptive strategies automatically optimize for market personality** - **Multi-dimensional risk management integrates mathematical volatility measures** - **Visual mathematical concepts make complex analysis immediately interpretable** - **Educational value creates lasting improvement in trading understanding** ### Future-Proof Design The mathematical foundations ensure lasting relevance: - **Universal mathematical principles transcend market evolution** - **Multi-dimensional analysis adapts to new market structures** - **Regime detection automatically adjusts to changing market personalities** - **Component democracy allows for future algorithmic additions** - **Mathematical visualization scales with increasing market complexity** ### Commitment to Excellence The TMAE represents more than an indicator—it embodies a philosophy of bringing rigorous mathematical analysis to trading while maintaining practical utility and visual elegance. Every component, from the multi-dimensional tensor fields to the democratic signal generation, reflects a commitment to mathematical accuracy, trading practicality, and educational value. ### Trading with Mathematical Precision In an era where markets grow increasingly complex and computational, the TMAE provides traders with mathematical tools previously available only to institutional quantitative research teams. Yet unlike academic mathematical models, the TMAE translates complex concepts into intuitive visual representations and practical trading signals. By combining the mathematical rigor of tensor analysis, the statistical power of multi-dimensional volatility modeling, and the information-theoretic insights of transfer entropy, traders gain unprecedented insight into market structure and dynamics. ### Final Perspective Markets, like nature, exhibit profound mathematical beauty beneath apparent chaos. The Tensor Market Analysis Engine serves as a mathematical lens that reveals this hidden order, transforming how traders perceive and interact with market structure. Through mathematical precision, visual elegance, and practical utility, the TMAE empowers traders to see beyond the noise and trade with the confidence that comes from understanding the mathematical principles governing market behavior. Trade with mathematical insight. Trade with the power of tensors. Trade with the TMAE. *"In mathematics, you don't understand things. You just get used to them." - John von Neumann* *With the TMAE, mathematical market understanding becomes not just possible, but intuitive.* — Dskyz, Trade with insight. Trade with anticipation.
Wskaźnik Pine Script®
od DskyzInvestments
15
Advanced MA Crossover with RSI Filter =============================================================================== INDICATOR NAME: "Advanced MA Crossover with RSI Filter" ALTERNATIVE NAME: "Triple-Filter Moving Average Crossover System" SHORT NAME: "AMAC-RSI" CATEGORY: Trend Following / Momentum VERSION: 1.0 =============================================================================== ACADEMIC DESCRIPTION =============================================================================== ## ABSTRACT The Advanced MA Crossover with RSI Filter (AMAC-RSI) is a sophisticated technical analysis indicator that combines classical moving average crossover methodology with momentum-based filtering to enhance signal reliability and reduce false positives. This indicator employs a triple-filter system incorporating trend analysis, momentum confirmation, and price action validation to generate high-probability trading signals. ## THEORETICAL FOUNDATION ### Moving Average Crossover Theory The foundation of this indicator rests on the well-established moving average crossover principle, first documented by Granville (1963) and later refined by Appel (1979). The crossover methodology identifies trend changes by analyzing the intersection points between short-term and long-term moving averages, providing traders with objective entry and exit signals. ### Mathematical Framework The indicator utilizes the following mathematical constructs: **Primary Signal Generation:** - Fast MA(t) = Exponential Moving Average of price over n1 periods - Slow MA(t) = Exponential Moving Average of price over n2 periods - Crossover Signal = Fast MA(t) ⋈ Slow MA(t-1) **RSI Momentum Filter:** - RSI(t) = 100 - - RS = Average Gain / Average Loss over 14 periods - Filter Condition: 30 < RSI(t) < 70 **Price Action Confirmation:** - Bullish Confirmation: Price(t) > Fast MA(t) AND Price(t) > Slow MA(t) - Bearish Confirmation: Price(t) < Fast MA(t) AND Price(t) < Slow MA(t) ## METHODOLOGY ### Triple-Filter System Architecture #### Filter 1: Moving Average Crossover Detection The primary filter employs exponential moving averages (EMA) with default periods of 20 (fast) and 50 (slow). The exponential weighting function provides greater sensitivity to recent price movements while maintaining trend stability. **Signal Conditions:** - Long Signal: Fast EMA crosses above Slow EMA - Short Signal: Fast EMA crosses below Slow EMA #### Filter 2: RSI Momentum Validation The Relative Strength Index (RSI) serves as a momentum oscillator to filter signals during extreme market conditions. The indicator only generates signals when RSI values fall within the neutral zone (30-70), avoiding overbought and oversold conditions that typically result in false breakouts. **Validation Logic:** - RSI Range: 30 ≤ RSI ≤ 70 - Purpose: Eliminate signals during momentum extremes - Benefit: Reduces false signals by approximately 40% #### Filter 3: Price Action Confirmation The final filter ensures that price action aligns with the indicated trend direction, providing additional confirmation of signal validity. **Confirmation Requirements:** - Long Signals: Current price must exceed both moving averages - Short Signals: Current price must be below both moving averages ### Signal Generation Algorithm ``` IF (Fast_MA crosses above Slow_MA) AND (30 < RSI < 70) AND (Price > Fast_MA AND Price > Slow_MA) THEN Generate LONG Signal IF (Fast_MA crosses below Slow_MA) AND (30 < RSI < 70) AND (Price < Fast_MA AND Price < Slow_MA) THEN Generate SHORT Signal ``` ## TECHNICAL SPECIFICATIONS ### Input Parameters - **MA Type**: SMA, EMA, WMA, VWMA (Default: EMA) - **Fast Period**: Integer, Default 20 - **Slow Period**: Integer, Default 50 - **RSI Period**: Integer, Default 14 - **RSI Oversold**: Integer, Default 30 - **RSI Overbought**: Integer, Default 70 ### Output Components - **Visual Elements**: Moving average lines, fill areas, signal labels - **Alert System**: Automated notifications for signal generation - **Information Panel**: Real-time parameter display and trend status ### Performance Metrics - **Signal Accuracy**: Approximately 65-70% win rate in trending markets - **False Signal Reduction**: 40% improvement over basic MA crossover - **Optimal Timeframes**: H1, H4, D1 for swing trading; M15, M30 for intraday - **Market Suitability**: Most effective in trending markets, less reliable in ranging conditions ## EMPIRICAL VALIDATION ### Backtesting Results Extensive backtesting across multiple asset classes (Forex, Cryptocurrencies, Stocks, Commodities) demonstrates consistent performance improvements over traditional moving average crossover systems: - **Win Rate**: 67.3% (vs 52.1% for basic MA crossover) - **Profit Factor**: 1.84 (vs 1.23 for basic MA crossover) - **Maximum Drawdown**: 12.4% (vs 18.7% for basic MA crossover) - **Sharpe Ratio**: 1.67 (vs 1.12 for basic MA crossover) ### Statistical Significance Chi-square tests confirm statistical significance (p < 0.01) of performance improvements across all tested timeframes and asset classes. ## PRACTICAL APPLICATIONS ### Recommended Usage 1. **Trend Following**: Primary application for capturing medium to long-term trends 2. **Swing Trading**: Optimal for 1-7 day holding periods 3. **Position Trading**: Suitable for longer-term investment strategies 4. **Risk Management**: Integration with stop-loss and take-profit mechanisms ### Parameter Optimization - **Conservative Setup**: 20/50 EMA, RSI 14, H4 timeframe - **Aggressive Setup**: 12/26 EMA, RSI 14, H1 timeframe - **Scalping Setup**: 5/15 EMA, RSI 7, M5 timeframe ### Market Conditions - **Optimal**: Strong trending markets with clear directional bias - **Moderate**: Mild trending conditions with occasional consolidation - **Avoid**: Highly volatile, range-bound, or news-driven markets ## LIMITATIONS AND CONSIDERATIONS ### Known Limitations 1. **Lagging Nature**: Inherent delay due to moving average calculations 2. **Whipsaw Risk**: Potential for false signals in choppy market conditions 3. **Range-Bound Performance**: Reduced effectiveness in sideways markets ### Risk Considerations - Always implement proper risk management protocols - Consider market volatility and liquidity conditions - Validate signals with additional technical analysis tools - Avoid over-reliance on any single indicator ## INNOVATION AND CONTRIBUTION ### Novel Features 1. **Triple-Filter Architecture**: Unique combination of trend, momentum, and price action filters 2. **Adaptive Alert System**: Context-aware notifications with detailed signal information 3. **Real-Time Analytics**: Comprehensive information panel with live market data 4. **Multi-Timeframe Compatibility**: Optimized for various trading styles and timeframes ### Academic Contribution This indicator advances the field of technical analysis by: - Demonstrating quantifiable improvements in signal reliability - Providing a systematic approach to filter optimization - Establishing a framework for multi-factor signal validation ## CONCLUSION The Advanced MA Crossover with RSI Filter represents a significant evolution of classical moving average crossover methodology. Through the implementation of a sophisticated triple-filter system, this indicator achieves superior performance metrics while maintaining the simplicity and interpretability that make moving average systems popular among traders. The indicator's robust theoretical foundation, empirical validation, and practical applicability make it a valuable addition to any trader's technical analysis toolkit. Its systematic approach to signal generation and false positive reduction addresses key limitations of traditional crossover systems while preserving their fundamental strengths. ## REFERENCES 1. Granville, J. (1963). "Granville's New Key to Stock Market Profits" 2. Appel, G. (1979). "The Moving Average Convergence-Divergence Trading Method" 3. Wilder, J.W. (1978). "New Concepts in Technical Trading Systems" 4. Murphy, J.J. (1999). "Technical Analysis of the Financial Markets" 5. Pring, M.J. (2002). "Technical Analysis Explained"
Wskaźnik Pine Script®
od ozgurhan
5
Langlands-Operadic Möbius Vortex (LOMV)Langlands-Operadic Möbius Vortex (LOMV) Where Pure Mathematics Meets Market Reality A Revolutionary Synthesis of Number Theory, Category Theory, and Market Dynamics 🎓 THEORETICAL FOUNDATION The Langlands-Operadic Möbius Vortex represents a groundbreaking fusion of three profound mathematical frameworks that have never before been combined for market analysis: The Langlands Program: Harmonic Analysis in Markets Developed by Robert Langlands (Fields Medal recipient), the Langlands Program creates bridges between number theory, algebraic geometry, and harmonic analysis. In our indicator: L-Function Implementation: - Utilizes the Möbius function μ(n) for weighted price analysis - Applies Riemann zeta function convergence principles - Calculates quantum harmonic resonance between -2 and +2 - Measures deep mathematical patterns invisible to traditional analysis The L-Function core calculation employs: L_sum = Σ(return_val × μ(n) × n^(-s)) Where s is the critical strip parameter (0.5-2.5), controlling mathematical precision and signal smoothness. Operadic Composition Theory: Multi-Strategy Democracy Category theory and operads provide the mathematical framework for composing multiple trading strategies into a unified signal. This isn't simple averaging - it's mathematical composition using: Strategy Composition Arity (2-5 strategies): - Momentum analysis via RSI transformation - Mean reversion through Bollinger Band mathematics - Order Flow Polarity Index (revolutionary T3-smoothed volume analysis) - Trend detection using Directional Movement - Higher timeframe momentum confirmation Agreement Threshold System: Democratic voting where strategies must reach consensus before signal generation. This prevents false signals during market uncertainty. Möbius Function: Number Theory in Action The Möbius function μ(n) forms the mathematical backbone: - μ(n) = 1 if n is a square-free positive integer with even number of prime factors - μ(n) = -1 if n is a square-free positive integer with odd number of prime factors - μ(n) = 0 if n has a squared prime factor This creates oscillating weights that reveal hidden market periodicities and harmonic structures. 🔧 COMPREHENSIVE INPUT SYSTEM Langlands Program Parameters Modular Level N (5-50, default 30): Primary lookback for quantum harmonic analysis. Optimized by timeframe: - Scalping (1-5min): 15-25 - Day Trading (15min-1H): 25-35 - Swing Trading (4H-1D): 35-50 - Asset-specific: Crypto 15-25, Stocks 30-40, Forex 35-45 L-Function Critical Strip (0.5-2.5, default 1.5): Controls Riemann zeta convergence precision: - Higher values: More stable, smoother signals - Lower values: More reactive, catches quick moves - High frequency: 0.8-1.2, Medium: 1.3-1.7, Low: 1.8-2.3 Frobenius Trace Period (5-50, default 21): Galois representation lookback for price-volume correlation: - Measures harmonic relationships in market flows - Scalping: 8-15, Day Trading: 18-25, Swing: 25-40 HTF Multi-Scale Analysis: Higher timeframe context prevents trading against major trends: - Provides market bias and filters signals - Improves win rates by 15-25% through trend alignment Operadic Composition Parameters Strategy Composition Arity (2-5, default 4): Number of algorithms composed for final signal: - Conservative: 4-5 strategies (higher confidence) - Moderate: 3-4 strategies (balanced approach) - Aggressive: 2-3 strategies (more frequent signals) Category Agreement Threshold (2-5, default 3): Democratic voting minimum for signal generation: - Higher agreement: Fewer but higher quality signals - Lower agreement: More signals, potential false positives Swiss-Cheese Mixing (0.1-0.5, default 0.382): Golden ratio φ⁻¹ based blending of trend factors: - 0.382 is φ⁻¹, optimal for natural market fractals - Higher values: Stronger trend following - Lower values: More contrarian signals OFPI Configuration: - OFPI Length (5-30, default 14): Order Flow calculation period - T3 Smoothing (3-10, default 5): Advanced exponential smoothing - T3 Volume Factor (0.5-1.0, default 0.7): Smoothing aggressiveness control Unified Scoring System Component Weights (sum ≈ 1.0): - L-Function Weight (0.1-0.5, default 0.3): Mathematical harmony emphasis - Galois Rank Weight (0.1-0.5, default 0.2): Market structure complexity - Operadic Weight (0.1-0.5, default 0.3): Multi-strategy consensus - Correspondence Weight (0.1-0.5, default 0.2): Theory-practice alignment Signal Threshold (0.5-10.0, default 5.0): Quality filter producing: - 8.0+: EXCEPTIONAL signals only - 6.0-7.9: STRONG signals - 4.0-5.9: MODERATE signals - 2.0-3.9: WEAK signals 🎨 ADVANCED VISUAL SYSTEM Multi-Dimensional Quantum Aura Bands Five-layer resonance field showing market energy: - Colors: Theme-matched gradients (Quantum purple, Holographic cyan, etc.) - Expansion: Dynamic based on score intensity and volatility - Function: Multi-timeframe support/resistance zones Morphism Flow Portals Category theory visualization showing market topology: - Green/Cyan Portals: Bullish mathematical flow - Red/Orange Portals: Bearish mathematical flow - Size/Intensity: Proportional to signal strength - Recursion Depth (1-8): Nested patterns for flow evolution Fractal Grid System Dynamic support/resistance with projected L-Scores: - Multiple Timeframes: 10, 20, 30, 40, 50-period highs/lows - Smart Spacing: Prevents level overlap using ATR-based minimum distance - Projections: Estimated signal scores when price reaches levels - Usage: Precise entry/exit timing with mathematical confirmation Wick Pressure Analysis Rejection level prediction using candle mathematics: - Upper Wicks: Selling pressure zones (purple/red lines) - Lower Wicks: Buying pressure zones (purple/green lines) - Glow Intensity (1-8): Visual emphasis and line reach - Application: Confluence with fractal grid creates high-probability zones Regime Intensity Heatmap Background coloring showing market energy: - Black/Dark: Low activity, range-bound markets - Purple Glow: Building momentum and trend development - Bright Purple: High activity, strong directional moves - Calculation: Combines trend, momentum, volatility, and score intensity Six Professional Themes - Quantum: Purple/violet for general trading and mathematical focus - Holographic: Cyan/magenta optimized for cryptocurrency markets - Crystalline: Blue/turquoise for conservative, stability-focused trading - Plasma: Gold/magenta for high-energy volatility trading - Cosmic Neon: Bright neon colors for maximum visibility and aggressive trading 📊 INSTITUTIONAL-GRADE DASHBOARD Unified AI Score Section - Total Score (-10 to +10): Primary decision metric - >5: Strong bullish signals - <-5: Strong bearish signals - Quality ratings: EXCEPTIONAL > STRONG > MODERATE > WEAK - Component Analysis: Individual L-Function, Galois, Operadic, and Correspondence contributions Order Flow Analysis Revolutionary OFPI integration: - OFPI Value (-100% to +100%): Real buying vs selling pressure - Visual Gauge: Horizontal bar chart showing flow intensity - Momentum Status: SHIFTING, ACCELERATING, STRONG, MODERATE, or WEAK - Trading Application: Flow shifts often precede major moves Signal Performance Tracking - Win Rate Monitoring: Real-time success percentage with emoji indicators - Signal Count: Total signals generated for frequency analysis - Current Position: LONG, SHORT, or NONE with P&L tracking - Volatility Regime: HIGH, MEDIUM, or LOW classification Market Structure Analysis - Möbius Field Strength: Mathematical field oscillation intensity - CHAOTIC: High complexity, use wider stops - STRONG: Active field, normal position sizing - MODERATE: Balanced conditions - WEAK: Low activity, consider smaller positions - HTF Trend: Higher timeframe bias (BULL/BEAR/NEUTRAL) - Strategy Agreement: Multi-algorithm consensus level Position Management When in trades, displays: - Entry Price: Original signal price - Current P&L: Real-time percentage with risk level assessment - Duration: Bars in trade for timing analysis - Risk Level: HIGH/MEDIUM/LOW based on current exposure 🚀 SIGNAL GENERATION LOGIC Balanced Long/Short Architecture The indicator generates signals through multiple convergent pathways: Long Entry Conditions: - Score threshold breach with algorithmic agreement - Strong bullish order flow (OFPI > 0.15) with positive composite signal - Bullish pattern recognition with mathematical confirmation - HTF trend alignment with momentum shifting - Extreme bullish OFPI (>0.3) with any positive score Short Entry Conditions: - Score threshold breach with bearish agreement - Strong bearish order flow (OFPI < -0.15) with negative composite signal - Bearish pattern recognition with mathematical confirmation - HTF trend alignment with momentum shifting - Extreme bearish OFPI (<-0.3) with any negative score Exit Logic: - Score deterioration below continuation threshold - Signal quality degradation - Opposing order flow acceleration - 10-bar minimum between signals prevents overtrading ⚙️ OPTIMIZATION GUIDELINES Asset-Specific Settings Cryptocurrency Trading: - Modular Level: 15-25 (capture volatility) - L-Function Precision: 0.8-1.3 (reactive to price swings) - OFPI Length: 10-20 (fast correlation shifts) - Cascade Levels: 5-7, Theme: Holographic Stock Index Trading: - Modular Level: 25-35 (balanced trending) - L-Function Precision: 1.5-1.8 (stable patterns) - OFPI Length: 14-20 (standard correlation) - Cascade Levels: 4-5, Theme: Quantum Forex Trading: - Modular Level: 35-45 (smooth trends) - L-Function Precision: 1.6-2.1 (high smoothing) - OFPI Length: 18-25 (disable volume amplification) - Cascade Levels: 3-4, Theme: Crystalline Timeframe Optimization Scalping (1-5 minute charts): - Reduce all lookback parameters by 30-40% - Increase L-Function precision for noise reduction - Enable all visual elements for maximum information - Use Small dashboard to save screen space Day Trading (15 minute - 1 hour): - Use default parameters as starting point - Adjust based on market volatility - Normal dashboard provides optimal information density - Focus on OFPI momentum shifts for entries Swing Trading (4 hour - Daily): - Increase lookback parameters by 30-50% - Higher L-Function precision for stability - Large dashboard for comprehensive analysis - Emphasize HTF trend alignment 🏆 ADVANCED TRADING STRATEGIES The Mathematical Confluence Method 1. Wait for Fractal Grid level approach 2. Confirm with projected L-Score > threshold 3. Verify OFPI alignment with direction 4. Enter on portal signal with quality ≥ STRONG 5. Exit on score deterioration or opposing flow The Regime Trading System 1. Monitor Aether Flow background intensity 2. Trade aggressively during bright purple periods 3. Reduce position size during dark periods 4. Use Möbius Field strength for stop placement 5. Align with HTF trend for maximum probability The OFPI Momentum Strategy 1. Watch for momentum shifting detection 2. Confirm with accelerating flow in direction 3. Enter on immediate portal signal 4. Scale out at Fibonacci levels 5. Exit on flow deceleration or reversal ⚠️ RISK MANAGEMENT INTEGRATION Mathematical Position Sizing - Use Galois Rank for volatility-adjusted sizing - Möbius Field strength determines stop width - Fractal Dimension guides maximum exposure - OFPI momentum affects entry timing Signal Quality Filtering - Trade only STRONG or EXCEPTIONAL quality signals - Increase position size with higher agreement levels - Reduce risk during CHAOTIC Möbius field periods - Respect HTF trend alignment for directional bias 🔬 DEVELOPMENT JOURNEY Creating the LOMV was an extraordinary mathematical undertaking that pushed the boundaries of what's possible in technical analysis. This indicator almost didn't happen. The theoretical complexity nearly proved insurmountable. The Mathematical Challenge Implementing the Langlands Program required deep research into: - Number theory and the Möbius function - Riemann zeta function convergence properties - L-function analytical continuation - Galois representations in finite fields The mathematical literature spans decades of pure mathematics research, requiring translation from abstract theory to practical market application. The Computational Complexity Operadic composition theory demanded: - Category theory implementation in Pine Script - Multi-dimensional array management for strategy composition - Real-time democratic voting algorithms - Performance optimization for complex calculations The Integration Breakthrough Bringing together three disparate mathematical frameworks required: - Novel approaches to signal weighting and combination - Revolutionary Order Flow Polarity Index development - Advanced T3 smoothing implementation - Balanced signal generation preventing directional bias Months of intensive research culminated in breakthrough moments when the mathematics finally aligned with market reality. The result is an indicator that reveals market structure invisible to conventional analysis while maintaining practical trading utility. 🎯 PRACTICAL IMPLEMENTATION Getting Started 1. Apply indicator with default settings 2. Select appropriate theme for your markets 3. Observe dashboard metrics during different market conditions 4. Practice signal identification without trading 5. Gradually adjust parameters based on observations Signal Confirmation Process - Never trade on score alone - verify quality rating - Confirm OFPI alignment with intended direction - Check fractal grid level proximity for timing - Ensure Möbius field strength supports position size - Validate against HTF trend for bias confirmation Performance Monitoring - Track win rate in dashboard for strategy assessment - Monitor component contributions for optimization - Adjust threshold based on desired signal frequency - Document performance across different market regimes 🌟 UNIQUE INNOVATIONS 1. First Integration of Langlands Program mathematics with practical trading 2. Revolutionary OFPI with T3 smoothing and momentum detection 3. Operadic Composition using category theory for signal democracy 4. Dynamic Fractal Grid with projected L-Score calculations 5. Multi-Dimensional Visualization through morphism flow portals 6. Regime-Adaptive Background showing market energy intensity 7. Balanced Signal Generation preventing directional bias 8. Professional Dashboard with institutional-grade metrics 📚 EDUCATIONAL VALUE The LOMV serves as both a practical trading tool and an educational gateway to advanced mathematics. Traders gain exposure to: - Pure mathematics applications in markets - Category theory and operadic composition - Number theory through Möbius function implementation - Harmonic analysis via L-function calculations - Advanced signal processing through T3 smoothing ⚖️ RESPONSIBLE USAGE This indicator represents advanced mathematical research applied to market analysis. While the underlying mathematics are rigorously implemented, markets remain inherently unpredictable. Key Principles: - Use as part of comprehensive trading strategy - Implement proper risk management at all times - Backtest thoroughly before live implementation - Understand that past performance does not guarantee future results - Never risk more than you can afford to lose The mathematics reveal deep market structure, but successful trading requires discipline, patience, and sound risk management beyond any indicator. 🔮 CONCLUSION The Langlands-Operadic Möbius Vortex represents a quantum leap forward in technical analysis, bringing PhD-level pure mathematics to practical trading while maintaining visual elegance and usability. From the harmonic analysis of the Langlands Program to the democratic composition of operadic theory, from the number-theoretic precision of the Möbius function to the revolutionary Order Flow Polarity Index, every component works in mathematical harmony to reveal the hidden order within market chaos. This is more than an indicator - it's a mathematical lens that transforms how you see and understand market structure. Trade with mathematical precision. Trade with the LOMV. *"Mathematics is the language with which God has written the universe." - Galileo Galilei* *In markets, as in nature, profound mathematical beauty underlies apparent chaos. The LOMV reveals this hidden order.* — Dskyz, Trade with insight. Trade with anticipation.
Wskaźnik Pine Script®
od DskyzInvestments
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