Machine Learning + IchimokuIchimoku Cloud + Machine Learning Levels is an advanced indicator that merges a classic trend tool with machine-learned supply & demand zones. Combining the two can help traders identify trends and key price zones with greater confidence when both signals align!
How it Works
The Ichimoku Cloud component identifies the trend direction and momentum at a glance – it shows support/resistance areas via its cloud (Kumo) and signals potential trend changes when the Tenkan-sen and Kijun-sen lines cross. Meanwhile, the Machine Learning module analyzes historical price data to project potential support and resistance levels (displayed as horizontal lines) that the algorithm deems significant. By combining these, the script offers a two-layer confirmation: Ichimoku outlines the broader trend and equilibrium, while the ML levels pinpoint specific price levels where the price may react. For example, if price is above the Ichimoku Cloud (uptrend) and also near an ML-predicted support, the confluence of these signals strengthens the case for a bounce.
How to Use
Apply the indicator to a chart like any other TradingView script. It works on multiple asset classes (see supported list below). Once added:
Ichimoku Lines
Tenkan-sen (Blue): Short-term average reflecting recent highs/lows.
Kijun-sen (Red): Medium-term baseline for support/resistance.
Senkou Span A (Green) & Senkou Span B (Orange) form the “Cloud” (Kumo). Price above the Cloud often signals a bullish environment; price below it can signal a bearish environment.
Chikou Span (Purple): Plots current closing price shifted back, helping gauge momentum vs. past price.
ML-Predicted Support/Resistance Lines (Green/Red Horizontal Lines)
Green Horizontal Lines – Potential support zones.
Red Horizontal Lines – Potential resistance zones.
These dynamically adjust based on the specific asset and are updated as new historical data becomes available.
Password (for Advanced Features)
In the indicator’s Settings, there is an input field labeled “Password.” The password corresponds to the ticker(s) listed below.
Stocks
TSLA, NVDA, AAPL, AMZN, PLTR, AMD, META, MSFT, MSTR, GOOG, GME, COIN, NFLX, BABA, UBER, HOOD, NKE
Cryptocurrencies
ETH, BTC, SOL, BNB, XRP, ADA, DOT, DOGE, LTC, JUP, LINK, INJ, FET, SAND, HBAR, TRX, SHIB, UNI
(If you attach the indicator to any unlisted ticker, you will only see the Ichimoku Cloud.)
Why It’s Unique
This script is a fresh take on market analysis – it’s original in fusing Ichimoku’s visual trend mapping with machine learning. The Ichimoku framework provides time-proven trend insight, and the ML levels add forward-looking context specific to each asset. By uniting them, the indicator aims to filter out false signals and highlight high-probability zones. No repainting occurs: Ichimoku values are based on closed data, and ML levels are computed from historical patterns (they do not retroactively change).
Ichimoku Cloud + Machine Learning Levels offers an informative blend of old and new analysis techniques. It clearly shows where price is relative to trend (via Ichimoku) and where it might react in the future (via ML levels). Use it to gain a richer view of the market’s behavior. I hope this indicator provides valuable insights for your trading decisions. Happy trading!
Wyszukaj w skryptach "文华财经tick价格"
Futures Open/High/Low TablesAdds (up to) 3 tables to a chart, displaying Open/High/Low data for today (RTH and extended hours), yesterday, and the current week / month -- to help with intraday analysis of a futures ticker.
The tables only appear on intraday charts (5min, 30min, etc). On a Daily/Weekly/etc chart they are not calculated or shown.
In addition to Open/High/Low, the "Current" table in the top-right shows a live measurement of # of points from the open, the RTH open, and the highs/lows.
Lastly, the 9:30am ET open and the 4pm RTH close are by default marked with a shaded background (on intraday charts) for easy visual reference, and also to help with adjusting the session time to accommodate time zone issues if they occur.
Tested on ES in Eastern Time Zone, but should work on any futures instrument and any time zone by adjusting the Session Time setting.
Delta VolDelta Volume BTC - Multi Pair
Description The Delta Volume BTC - Multi Pair indicator visualizes the balance between buying and selling volume across multiple Bitcoin exchanges. By analyzing price action within each bar, it provides insight into underlying market pressure that traditional volume indicators miss. This indicator allows traders to:
Compare volume flow across Coinbase, Binance, and Binance Perpetual markets
Identify divergences between exchanges that may signal market shifts
Detect accumulation or distribution patterns through volume imbalances
View exchanges individually or in aggregate for comprehensive analysis
Calculation Methods The indicator offers three volume delta calculation methods:
VWAP Based (default):
price_range = high - low
buy_percent = (close - low) / price_range
sell_percent = (high - close) / price_range
delta = volume * (buy_percent - sell_percent)
This method distributes volume based on where price closed within the bar's range, providing a nuanced view of buying/selling pressure.
Tick Based :
delta = volume * sign(hlc3 - previous_hlc3)
This approach assigns volume based on the direction of typical price movement between bars, capturing momentum between periods.
Simple :
delta = close > open ? volume : close < open ? -volume : 0
A straightforward method that assigns positive volume to up bars and negative volume to down bars.
When Aggregate Mode is enabled, the indicator sums the volume deltas from all selected exchanges:
aggregate_delta = coinbase_delta + binance_delta + binance_perp_delta
Features
Multi-Exchange Support : Track volume delta across Coinbase, Binance, and Binance Perpetual futures
Advanced Calculation Methods : Choose between VWAP-based, tick-based, or simple volume delta algorithms
Flexible Display Options : Visualize as histogram, columns, area, or line charts
Customizable Colors : Distinct color schemes for each exchange and direction
Smoothing Options : Apply EMA, SMA, or WMA to reduce noise
Aggregate Mode : Combine all exchanges to see total market flow
How to Use
Individual Exchange Analysis : Uncheck "Aggregate Mode" to see each exchange separately, revealing where smart money may be positioning
Divergence Detection : Watch for one exchange showing buying while others show selling
Volume Trend Confirmation : Strong price moves should be accompanied by strong delta in the same direction
Liquidity Analysis : Compare spot vs futures volume delta to identify market sentiment shifts
The Delta Volume BTC - Multi Pair indicator helps identify the "hidden" buying and selling pressure that may not be apparent from price action alone, giving you an edge in understanding market dynamics across the Bitcoin ecosystem.
TPO IQ [TradingIQ]Hello Traders!
Introducing "TPO IQ"!
TPO IQ offers a Time Price Opportunity profile with several customization options that packs several related features to help traders navigate the generated profiles!
Features
TPO Profiles
Single Print identification
Initial Balance Identification
Can be anchored to timeframe change
Can be anchored to fixed time interval
Last profile detailed visuals
Customizable value area percentage
POC identification
Mid-point identification
TPO Profiles
A TPO profile is a market profile visualization that details how much time was spent at each price level throughout the time interval.
The image above further explains what a TPO Profile is!
Each letter corresponds to a candlestick. With this information, traders are able to visualize how much time was spent at each price area.
With customizable gradient colors, specifically in this example, blocks colored red are the earliest times in the profile, blocks colored green are in the beginning half of the time midpoint of the profile. Blue blocks represent the first half of the end of the time period, and purple blocks correspond to the end of the time period.
Please note that this form of TPO profile generation will only occur when the most recent profile uses less than 500 alphabet characters! If more than 500 characters are preset, TPO IQ will revert to using labels!
Initial Balance
TPO IQ also identifies the initial balance range and all alphabet characters that form within it!
The image above exemplifies this feature. The initial balance range is denoted by a a neon-blue line, with a blue circle showing the opening price. All characters within the initial balance range are highlighted blue, which is a feature that can be disabled with customizable colors.
POC
TPO IQ also identifies the point of control (POC) of the TPO Profile.
The point of control for the profile is labeled yellow by default, and shows where price spent the most time throughout the time period.
The image above shows the POC for the time period being identified by TPO IQ.
Value Area
TPO IQ also identifies the value area of the profile. A customizable percentage that is 70% by default, the value area of a TPO profile shows where price traded the majority of the time.
The image above further explains this feature. For this example, with the value area percentage being set to 70%, the value area high and value area low show the price zone that prices traded at 70% of the time throughout the profile.
TPO Midpoint
In addition to the POC, the TPO profile midpoint is also identified by TPO IQ.
The TPO midpoint simply corresponds to the middle price between the session's high and low!
Fixed Interval Mode
By default, TPO IQ recalculates every day, but this can also be changed to a customizable session time, such as 4 hours. If 4 hours is selected, then a new TPO profile will be generated every 4 hours.
However, in Fixed Interval mode, a TPO profile will be generated through a user-defined time range, such as 1300-1700.
In the image above, Fixed Interval mode is applied with a time range of 1300-1700 and, consequently, TPO IQ generates a new profile throughout every 1300-1700 time range!
This feature allows traders to specify time ranges of interest to generate TPO profiles for!
TPO Overview Label
The TPO overview label shows key statistics for the TPO profile generated throughout the trading session!
The "TPO Count" statistic shows how many alphabetical letters were generated for the profile, which is an adequate method to determine the session's volatility and price range.
The "Tick Levels" statistic shows how many tick levels were used to create the profile - another method to determine the volatility and price range of the session.
The "Top Letter" statistic shows which letter appears most throughout the profile. In this example, the top letter was "f", which means throughout creation of the profile, the letter "f" appeared the most!
And that's all for now!
If you have any feedback or new feature ideas for TPO IQ please feel free to share them with us!
Thank you traders!
AO/AC Trading Zones Strategy [Skyrexio] Overview
AO/AC Trading Zones Strategy leverages the combination of Awesome Oscillator (AO), Acceleration/Deceleration Indicator (AC), Williams Fractals, Williams Alligator and Exponential Moving Average (EMA) to obtain the high probability long setups. Moreover, strategy uses multi trades system, adding funds to long position if it considered that current trend has likely became stronger. Combination of AO and AC is used for creating so-called trading zones to create the signals, while Alligator and Fractal are used in conjunction as an approximation of short-term trend to filter them. At the same time EMA (default EMA's period = 100) is used as high probability long-term trend filter to open long trades only if it considers current price action as an uptrend. More information in "Methodology" and "Justification of Methodology" paragraphs. The strategy opens only long trades.
Unique Features
No fixed stop-loss and take profit: Instead of fixed stop-loss level strategy utilizes technical condition obtained by Fractals and Alligator to identify when current uptrend is likely to be over. In some special cases strategy uses AO and AC combination to trail profit (more information in "Methodology" and "Justification of Methodology" paragraphs)
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Multilayer trades opening system: strategy uses only 10% of capital in every trade and open up to 5 trades at the same time if script consider current trend as strong one.
Short and long term trend trade filters: strategy uses EMA as high probability long-term trend filter and Alligator and Fractal combination as a short-term one.
Methodology
The strategy opens long trade when the following price met the conditions:
1. Price closed above EMA (by default, period = 100). Crossover is not obligatory.
2. Combination of Alligator and Williams Fractals shall consider current trend as an upward (all details in "Justification of Methodology" paragraph)
3. Both AC and AO shall print two consecutive increasing values. At the price candle close which corresponds to this condition algorithm opens the first long trade with 10% of capital.
4. If combination of Alligator and Williams Fractals shall consider current trend has been changed from up to downtrend, all long trades will be closed, no matter how many trades has been opened.
5. If AO and AC both continue printing the rising values strategy opens the long trade on each candle close with 10% of capital while number of opened trades reaches 5.
6. If AO and AC both has printed 5 rising values in a row algorithm close all trades if candle's low below the low of the 5-th candle with rising AO and AC values in a row.
Script also has additional visuals. If second long trade has been opened simultaneously the Alligator's teeth line is plotted with the green color. Also for every trade in a row from 2 to 5 the label "Buy More" is also plotted just below the teeth line. With every next simultaneously opened trade the green color of the space between teeth and price became less transparent.
Strategy settings
In the inputs window user can setup strategy setting:
EMA Length (by default = 100, period of EMA, used for long-term trend filtering EMA calculation).
User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Let's explore the key concepts of this strategy and understand how they work together. We'll begin with the simplest: the EMA.
The Exponential Moving Average (EMA) is a type of moving average that assigns greater weight to recent price data, making it more responsive to current market changes compared to the Simple Moving Average (SMA). This tool is widely used in technical analysis to identify trends and generate buy or sell signals. The EMA is calculated as follows:
1.Calculate the Smoothing Multiplier:
Multiplier = 2 / (n + 1), Where n is the number of periods.
2. EMA Calculation
EMA = (Current Price) × Multiplier + (Previous EMA) × (1 − Multiplier)
In this strategy, the EMA acts as a long-term trend filter. For instance, long trades are considered only when the price closes above the EMA (default: 100-period). This increases the likelihood of entering trades aligned with the prevailing trend.
Next, let’s discuss the short-term trend filter, which combines the Williams Alligator and Williams Fractals. Williams Alligator
Developed by Bill Williams, the Alligator is a technical indicator that identifies trends and potential market reversals. It consists of three smoothed moving averages:
Jaw (Blue Line): The slowest of the three, based on a 13-period smoothed moving average shifted 8 bars ahead.
Teeth (Red Line): The medium-speed line, derived from an 8-period smoothed moving average shifted 5 bars forward.
Lips (Green Line): The fastest line, calculated using a 5-period smoothed moving average shifted 3 bars forward.
When the lines diverge and align in order, the "Alligator" is "awake," signaling a strong trend. When the lines overlap or intertwine, the "Alligator" is "asleep," indicating a range-bound or sideways market. This indicator helps traders determine when to enter or avoid trades.
Fractals, another tool by Bill Williams, help identify potential reversal points on a price chart. A fractal forms over at least five consecutive bars, with the middle bar showing either:
Up Fractal: Occurs when the middle bar has a higher high than the two preceding and two following bars, suggesting a potential downward reversal.
Down Fractal: Happens when the middle bar shows a lower low than the surrounding two bars, hinting at a possible upward reversal.
Traders often use fractals alongside other indicators to confirm trends or reversals, enhancing decision-making accuracy.
How do these tools work together in this strategy? Let’s consider an example of an uptrend.
When the price breaks above an up fractal, it signals a potential bullish trend. This occurs because the up fractal represents a shift in market behavior, where a temporary high was formed due to selling pressure. If the price revisits this level and breaks through, it suggests the market sentiment has turned bullish.
The breakout must occur above the Alligator’s teeth line to confirm the trend. A breakout below the teeth is considered invalid, and the downtrend might still persist. Conversely, in a downtrend, the same logic applies with down fractals.
In this strategy if the most recent up fractal breakout occurs above the Alligator's teeth and follows the last down fractal breakout below the teeth, the algorithm identifies an uptrend. Long trades can be opened during this phase if a signal aligns. If the price breaks a down fractal below the teeth line during an uptrend, the strategy assumes the uptrend has ended and closes all open long trades.
By combining the EMA as a long-term trend filter with the Alligator and fractals as short-term filters, this approach increases the likelihood of opening profitable trades while staying aligned with market dynamics.
Now let's talk about the trading zones concept and its signals. To understand this we need to briefly introduce what is AO and AC. The Awesome Oscillator (AO), developed by Bill Williams, is a momentum indicator designed to measure market momentum by contrasting recent price movements with a longer-term historical perspective. It helps traders detect potential trend reversals and assess the strength of ongoing trends.
The formula for AO is as follows:
AO = SMA5(Median Price) − SMA34(Median Price)
where:
Median Price = (High + Low) / 2
SMA5 = 5-period Simple Moving Average of the Median Price
SMA 34 = 34-period Simple Moving Average of the Median Price
The Acceleration/Deceleration (AC) Indicator, introduced by Bill Williams, measures the rate of change in market momentum. It highlights shifts in the driving force of price movements and helps traders spot early signs of trend changes. The AC Indicator is particularly useful for identifying whether the current momentum is accelerating or decelerating, which can indicate potential reversals or continuations. For AC calculation we shall use the AO calculated above is the following formula:
AC = AO − SMA5(AO) , where SMA5(AO)is the 5-period Simple Moving Average of the Awesome Oscillator
When the AC is above the zero line and rising, it suggests accelerating upward momentum.
When the AC is below the zero line and falling, it indicates accelerating downward momentum.
When the AC is below zero line and rising it suggests the decelerating the downtrend momentum. When AC is above the zero line and falling, it suggests the decelerating the uptrend momentum.
Now let's discuss the trading zones concept and how it can create the signal. Zones are created by the combination of AO and AC. We can divide three zone types:
Greed zone: when the AO and AC both are rising
Red zone: when the AO and AC both are decreasing
Gray zone: when one of AO or AC is rising, the other is falling
Gray zone is considered as uncertainty. AC and AO are moving in the opposite direction. Strategy skip such price action to decrease the chance to stuck in the losing trade during potential sideways. Red zone is also not interesting for the algorithm because both indicators consider the trend as bearish, but strategy opens only long trades. It is waiting for the green zone to increase the chance to open trade in the direction of the potential uptrend. When we have 2 candles in a row in the green zone script executes a long trade with 10% of capital.
Two green zone candles in a row is considered by algorithm as a bullish trend, but now so strong, that's the reason why trade is going to be closed when the combination of Alligator and Fractals will consider the the trend change from bullish to bearish. If id did not happens, algorithm starts to count the green zone candles in a row. When we have 5 in a row script change the trade closing condition. Such situation is considered is a high probability strong bull market and all trades will be closed if candle's low will be lower than fifth green zone candle's low. This is used to increase probability to secure the profit. If long trades are initiated, the strategy continues utilizing subsequent signals until the total number of trades reaches a maximum of 5. Each trade uses 10% of capital.
Why we use trading zones signals? If currently strategy algorithm considers the high probability of the short-term uptrend with the Alligator and Fractals combination pointed out above and the long-term trend is also suggested by the EMA filter as bullish. Rising AC and AO values in the direction of the most likely main trend signaling that we have the high probability of the fastest bullish phase on the market. The main idea is to take part in such rapid moves and add trades if this move continues its acceleration according to indicators.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.12.31. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 10%
Maximum Single Position Loss: -9.49%
Maximum Single Profit: +24.33%
Net Profit: +4374.70 USDT (+43.75%)
Total Trades: 278 (39.57% win rate)
Profit Factor: 2.203
Maximum Accumulated Loss: 668.16 USDT (-5.43%)
Average Profit per Trade: 15.74 USDT (+1.37%)
Average Trade Duration: 60 hours
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 4h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
IronCondor 10am 30TF by RMThe IronCondor 10am 30TF indicator shows Iron Condor trades win rate over a large number of days.
The default ETFs in this indicators are "QQQ", "SPY", "RUT" , "CBTX" and "SPX", other entries have not been tested.
Iron Condor quick explanation:
- Iron Condors trades have four options, generally, are based around a Midpoint price (Current Market Price Strike) and
- Two equally distances Strikes for the SELL components (called the Body of the Iron Condor)
- Further away from the two SELLs, another Two BUYs for protection (not considered in this indicator)
- Iron Condors are used for Passive Income based on small gains most of the time.
The IronCondor 10am 30TF has its logic created based on the premises that:
- Most days the market prices stay within a range.
- As example the S&P market prices would stay within 1% on about 80% of the time
- The moving markets (bullish or bearish) occur about 20% of the time
- The biggest market price volatility generally occurs before market opens and then around the first hour or so of trade in the day.
- After the first hour or so of the market the prices would be most likely to stay within a range.
The operation is simple:
- At the Trade Star time in the day (say 10:30 Hrs.) draws a vertical yellow line, then
- Creates two blue horizontal lines for the SELL limits in the Iron Condor Body, at +/- 1% price boundary (check Ticker list below for values)
- At the Trade End time (say 16:00 Hrs.) checks that none of the SELL limits have been broken by highs or lows during the trade day
(The check is done calculating at Trade End time the high/lows 10 bars back for 30 min TF - timeframe)
- There is a label at each Trade End time with Win/Loss and Body value.
- There is one final label with overall calculated past performance in Win percentage out of 'n' trades
Defaults and User Entries:
- The User can modify the Midpoint price called 'IronCondor Midpoint STRIKE' (default is the Candle Close at the selected time)
- The User can modify the Body value called 'IronCondor Body' (default is the Ticker's selected value as per list below)
"QQQ" or "SPY" Body = 5
"RUT" or "CBTX" Body = 20
"SPX" Body = 60
* Disclaimer: This is not a Financial tool, it cannot used as any kind of advice to invest or risk moneys in any market,
Markets are volatile in nature - with little or no warning - and will drain your account if you are not careful.
Use only as an academic demonstrator => * Use at your own risk *
Opening Range Breakout (ORB)This is an Opening Range Break indicator. Best if used on a 5 minute chart. It plots the opening 30 minutes high and low of a ticker. (meaning mostly for stocks, options, etfs) and then it alerts a buy signal upon break of opening high and a sell signal upon break of opening low. This is a day trading type of indicator and there is a new opening range everyday.
Triad Trade MatrixOverview
Triad Trade Matrix is an advanced multi-strategy indicator built using Pine Script v5. It is designed to simultaneously track and display key trading metrics for three distinct trading styles on a single chart:
Swing Trading (Swing Supreme):
This mode captures longer-term trends and is designed for trades that typically span several days. It uses customizable depth and deviation parameters to determine swing signals.
Day Trading (Day Blaze):
This mode focuses on intraday price movements. It generates signals that are intended to be executed within a single trading session. The parameters for depth and deviation are tuned to capture more frequent, shorter-term moves.
Scalping (Scalp Surge):
This mode is designed for very short-term trades where quick entries and exits are key. It uses more sensitive parameters to detect rapid price movements suitable for scalping strategies.
Each trading style is represented by its own merged table that displays real-time metrics. The tables update automatically as new trading signals are generated.
Key Features
Multi-Style Tracking:
Swing Supreme (Large): For swing trading; uses a purple theme.
Day Blaze (Medium): For day trading; uses an orange theme.
Scalp Surge (Small): For scalping; uses a green theme.
Real-Time Metrics:
Each table displays key trade metrics including:
Entry Price: The price at which the trade was entered.
Exit Price: The price at which the previous trade was exited.
Position Size: Calculated as the account size divided by the entry price.
Direction: Indicates whether the trade is “Up” (long) or “Down” (short).
Time: The time when the trade was executed (formatted to hours and minutes).
Wins/Losses: The cumulative number of winning and losing trades.
Current Price & PnL: The current price on the chart and the profit/loss computed relative to the entry price.
Duration: The number of bars that the trade has been open.
History Column: A merged summary column that shows the most recent trade’s details (entry, exit, and result).
Customizability:
Column Visibility: Users can toggle individual columns (Ticker, Timeframe, Entry, Exit, etc.) on or off according to their preference.
Appearance Settings: You can customize the table border width, frame color, header background, and text colors.
History Toggle: The merged history column can be enabled or disabled.
Chart Markers: There is an option to show or hide chart markers (labels and lines) that indicate trade entries and exits on the chart.
Trade History Management:
The indicator maintains a rolling history (up to three recent trades per trading style) and displays the latest summary in the merged table.
This history column provides a quick reference to recent performance.
How It Works
Signal Generation & Trade Metrics
Trade Entry/Exit Calculation:
For each trading style, the indicator uses built-in functions (such as ta.lowestbars and ta.highestbars) to analyze price movements. Based on a customizable "depth" and "deviation" parameter, it determines the point of entry for a trade.
Swing Supreme: Uses larger depth/deviation values to capture swing trends.
Day Blaze: Uses intermediate values for intraday moves.
Scalp Surge: Uses tighter parameters to pick up rapid price changes.
Metrics Update:
When a new trade signal is generated (i.e., when the trade entry price is updated), the indicator calculates:
The current PnL as the difference between the current price and the entry price (or vice versa, depending on the trade direction).
The duration as the number of bars since the trade was opened.
The position size using the formula: accountSize / entryPrice.
History Recording:
Each time a new trade is triggered (i.e., when the entry price is updated), a summary string is created (showing entry, exit, and win/loss status) and appended to the corresponding trade history array. The merged table then displays the latest summary from this history.
Table Display
Merged Table Structure:
Each trading style (Swing Supreme, Day Blaze, and Scalp Surge) is represented by a table that has 15 columns. The columns are:
Trade Type (e.g., Swing Supreme)
Ticker
Timeframe
Entry Price
Exit Price
Position Size
Direction
Time of Entry
Account Size
Wins
Losses
Current Price
Current PnL
Duration (in bars)
History (the latest trade summary)
User Customization:
Through the settings panel, users can choose which columns to display.
If a column is toggled off, its cells will remain blank, allowing traders to focus on the metrics that matter most to them.
Appearance & Themes:
The table headers and cell backgrounds are customizable via color inputs. The trading style names are color-coded:
Swing Supreme (Large): Uses a purple theme.
Day Blaze (Medium): Uses an orange theme.
Scalp Surge (Small): Uses a green theme.
How to Use the Indicator
Add the Indicator to Your Chart:
Once published, add "Triad Trade Matrix" to your TradingView chart.
Configure the Settings:
Adjust the Account Size to match your trading capital.
Use the Depth and Deviation inputs for each trading style to fine-tune the signal sensitivity.
Toggle the Chart Markers on if you want visual entry/exit markers on the chart.
Customize which columns are visible via the column visibility toggles.
Enable or disable the History Column to show the merged trade history in the table.
Adjust the appearance settings (colors, border width, etc.) to suit your chart background and preferences.
Interpret the Tables:
Swing Supreme:
This table shows metrics for swing trades.
Look for changes in entry price, PnL, and trade duration to monitor longer-term moves.
Day Blaze:
This table tracks day trading activity.It will update more frequently, reflecting intraday trends.
Scalp Surge:
This table is dedicated to scalping signals.Use it to see quick entry/exit data and rapid profit/loss changes.
The History column (if enabled) gives you a snapshot of the most recent trade (e.g., "E:123.45 X:124.00 Up Win").
Use allerts:
The indicator includes alert condition for new trade entries(both long and short)for each trading style.
Summary:
Triad Trade Matrix provides an robust,multi-dimensional view of your trading performance across swing trading, day trading, and scalping.
Best to be used whith my other indicators
True low high
Vma Ext_Adv_CustomTbl
This indicator is ideal for traders who wish to monitor multiple trading styles simultaneously, with a clear, technical, and real-time display of performance metrics.
Happy Trading!
ELHAI Futures Trend Checker (ES, NQ, YM)The ELHAI Futures Trend Checker is a powerful TradingView indicator designed for futures traders who want to monitor the trend synchronization of the three major U.S. futures indices:
✅ E-mini S&P 500 (ES1!)
✅ E-mini Nasdaq 100 (NQ1!)
✅ E-mini Dow Jones (YM1!)
This indicator checks whether all three futures indices are bullish or bearish during each candle formation. If one of them is out of sync (e.g., two indices are bullish while one is bearish), the indicator triggers an alert and highlights the background in red, helping traders identify potential market indecision or divergence.
Key Features
📌 Designed for Futures Traders – Focuses on ES, NQ, and YM futures contracts.
📌 Live Market Monitoring – Works in real-time and updates dynamically with each tick.
📌 Bullish/Bearish Trend Confirmation – Detects when all three indices are in sync.
📌 Mismatch Detection – Alerts you when at least one index is out of trend.
📌 Custom Alerts – Set up TradingView alerts to be notified instantly when a trend mismatch occurs.
📌 Visual Background Highlight – A red background warns of a market divergence.
How It Works
The script retrieves open and close prices for ES, NQ, and YM.
Determines whether each futures index is bullish (close > open) or bearish (close < open).
If all three indices are bullish or all are bearish, it remains neutral.
If one index is different, an alert is triggered and the background turns red.
How to Use
Apply the indicator to your TradingView chart.
Choose any timeframe – Works well on intraday, daily, or higher timeframes.
Enable alerts: Go to Alerts → Create Alert, select "Futures Trend Mismatch", and set your preferred alert frequency.
Use alongside other indicators like moving averages, RSI, or MACD for better trade confirmation.
Best Use Cases
✔ Day traders & scalpers – Quickly spot market divergence in live trading.
✔ Swing traders – Identify when futures markets lose synchronization.
✔ Trend followers – Confirm if all major futures markets are aligned before making a move.
Final Notes
This indicator was built for Elhai to provide real-time trend analysis across major U.S. futures indices. Use it as a confirmation tool to improve market timing and decision-making.
TTM FCFF Yield %An indicator that shows the Free Cash Flow yield daily for the underlying ticker. Useful for when you need to screen for ideas, or the news just broke out and you want to make a calculated purchase - rather than buying at whatever price it is at the moment.
Green line tracks daily Free Cash Flow yield to Enterprise Value.
Where Free Cash Flow is defined as = Cashflow from Operations + Depreciation and Amortization (from the income statement) - Capital Expenditure (fixed assets) - Change in Working Capital
And where Enterprise Value is defined as = Market Capitalization + Net Debt
Red line tracks Free Cash Flow of financial year and what FCFF yield does that equate to if the stock current trades at the price right now.
Reminder: When working with international equities. Be mindful of whether they report FQ or FH. For example, France only reports FH, so it's better to use TTM FHFree Cash Flow results. If you didn't toggle FH in the indicator settings, it will automatically set as FQ and it will not show anything.
GLHF
Enhanced SPX and BTC Overlay with EMASPX-BTC Momentum Gauge and EMA Cross Indicator
Thorough Analysis:
• Combined Overlay (Green/Red Line):
o Function: Plots a wide line over the price chart, representing a composite of SPX and BTC dynamics adjusted by volume data.
o Color Coding:
Green: Indicates bullish conditions when the combined value exceeds its 10-period SMA and Bitcoin volume increases.
Red: Signals bearish conditions when the combined value drops below its 10-period SMA and Bitcoin volume decreases.
o Line Characteristics:
Width: Set at 8 for high visibility.
Transparency: 86% for both colors to overlay without obscuring candlesticks.
Scaling: Uses a factor of 0.02446 to amplify movements, making trend changes more noticeable.
• Continuous Bright Red and Green Lines:
o 20-period EMA of Current Ticker (Red):
Purpose: Acts as a medium-term trend indicator, smoothing price data to reflect the asset's general direction over time.
Color: Bright red for easy identification.
Transparency: 60% to keep it visible but not overpowering.
o 5-period EMA of BTC (Green):
Purpose: Provides insights into short-term Bitcoin momentum, capturing rapid changes in market sentiment.
Color: Bright green to distinguish from the red EMA.
Transparency: 30% for high visibility against price movements.
Detailed Analysis of the EMA Cross:
• Crossing Points:
o Bullish Crossover:
Occurs when the 5-period BTC EMA (green) moves above the 20-period EMA of the current ticker (red).
Suggests that Bitcoin's short-term momentum is gaining strength relative to the asset's medium-term trend, potentially signaling an upcoming uptrend or strengthening of an existing one.
o Bearish Crossover:
When the green line falls below the red, it indicates that Bitcoin's immediate momentum is weakening compared to the asset's medium-term trend, which might precede a downtrend or confirm one.
• Early Trade Signals:
o Entry/Exit Points:
These crossovers can guide traders in making timely decisions to enter or exit trades, especially when corroborated by the combined overlay's color.
o Confirmation:
EMA crossovers can confirm trends indicated by the combined overlay. For example, a bullish crossover with a green combined line could validate a buying opportunity.
o Volatility Insights:
The rapid shifts in Bitcoin's 5-period EMA highlight potential volatility spikes, offering an additional layer of market analysis, particularly useful in volatile markets.
• Strategic Use:
o Multi-Market Insight: The script integrates data from both traditional (SPX) and crypto (BTC) markets, allowing for a more comprehensive analysis of market conditions.
o Decision-Making: Provides traders with visual cues for market sentiment, trend direction, and potential reversals, enhancing strategic trading decisions.
o Trend Confirmation: The combination of EMA crossovers and the overlay's color changes offers a multi-faceted approach to trend confirmation or divergence.
In Summary:
• This script merges elements of traditional stock market analysis with cryptocurrency dynamics, utilizing color changes, line thickness, and EMA crossovers to visually communicate market conditions, offering traders a robust tool for analyzing and acting on market movements.
200WMA ScreenerDescription:
This custom indicator helps identify stocks trading below their 200-week moving average (200WMA), a key technical indicator often used to analyze long-term trends. The script calculates the 200WMA using weekly close prices and provides the following features:
Visual Plot: Displays the 200WMA as a smooth line on the chart for easy trend analysis.
Background Highlight: Automatically highlights the chart background when the current price is below the 200WMA, signaling a potential bearish trend or undervalued stock.
Alert System: Includes an alert condition to notify you when a stock trades below its 200WMA, so you never miss an opportunity.
Compatibility: Works across all assets (stocks, forex, crypto) and automatically adapts to the selected ticker.
This script is ideal for traders and investors looking for long-term opportunities, identifying potential trend reversals, or spotting undervalued stocks.
Cash and Carry: Annualized BTC Basis (Parametric)This indicator calculates the annualized BTC basis (premium or discount) between a specified futures contract and a given spot symbol. You can customize the spot ticker, the futures ticker, and the exact expiration date/time. As time moves toward expiration, the annualized yield (basis) will adjust accordingly. Ideal for monitoring potential arbitrage or cash-and-carry opportunities!
Hybrid Adaptive Double Exponential Smoothing🙏🏻 This is HADES (Hybrid Adaptive Double Exponential Smoothing) : fully data-driven & adaptive exponential smoothing method, that gains all the necessary info directly from data in the most natural way and needs no subjective parameters & no optimizations. It gets applied to data itself -> to fit residuals & one-point forecast errors, all at O(1) algo complexity. I designed it for streaming high-frequency univariate time series data, such as medical sensor readings, orderbook data, tick charts, requests generated by a backend, etc.
The HADES method is:
fit & forecast = a + b * (1 / alpha + T - 1)
T = 0 provides in-sample fit for the current datum, and T + n provides forecast for n datapoints.
y = input time series
a = y, if no previous data exists
b = 0, if no previous data exists
otherwise:
a = alpha * y + (1 - alpha) * a
b = alpha * (a - a ) + (1 - alpha) * b
alpha = 1 / sqrt(len * 4)
len = min(ceil(exp(1 / sig)), available data)
sig = sqrt(Absolute net change in y / Sum of absolute changes in y)
For the start datapoint when both numerator and denominator are zeros, we define 0 / 0 = 1
...
The same set of operations gets applied to the data first, then to resulting fit absolute residuals to build prediction interval, and finally to absolute forecasting errors (from one-point ahead forecast) to build forecasting interval:
prediction interval = data fit +- resoduals fit * k
forecasting interval = data opf +- errors fit * k
where k = multiplier regulating intervals width, and opf = one-point forecasts calculated at each time t
...
How-to:
0) Apply to your data where it makes sense, eg. tick data;
1) Use power transform to compensate for multiplicative behavior in case it's there;
2) If you have complete data or only the data you need, like the full history of adjusted close prices: go to the next step; otherwise, guided by your goal & analysis, adjust the 'start index' setting so the calculations will start from this point;
3) Use prediction interval to detect significant deviations from the process core & make decisions according to your strategy;
4) Use one-point forecast for nowcasting;
5) Use forecasting intervals to ~ understand where the next datapoints will emerge, given the data-generating process will stay the same & lack structural breaks.
I advise k = 1 or 1.5 or 4 depending on your goal, but 1 is the most natural one.
...
Why exponential smoothing at all? Why the double one? Why adaptive? Why not Holt's method?
1) It's O(1) algo complexity & recursive nature allows it to be applied in an online fashion to high-frequency streaming data; otherwise, it makes more sense to use other methods;
2) Double exponential smoothing ensures we are taking trends into account; also, in order to model more complex time series patterns such as seasonality, we need detrended data, and this method can be used to do it;
3) The goal of adaptivity is to eliminate the window size question, in cases where it doesn't make sense to use cumulative moving typical value;
4) Holt's method creates a certain interaction between level and trend components, so its results lack symmetry and similarity with other non-recursive methods such as quantile regression or linear regression. Instead, I decided to base my work on the original double exponential smoothing method published by Rob Brown in 1956, here's the original source , it's really hard to find it online. This cool dude is considered the one who've dropped exponential smoothing to open access for the first time🤘🏻
R&D; log & explanations
If you wanna read this, you gotta know, you're taking a great responsability for this long journey, and it gonna be one hell of a trip hehe
Machine learning, apprentissage automatique, машинное обучение, digital signal processing, statistical learning, data mining, deep learning, etc., etc., etc.: all these are just artificial categories created by the local population of this wonderful world, but what really separates entities globally in the Universe is solution complexity / algorithmic complexity.
In order to get the game a lil better, it's gonna be useful to read the HTES script description first. Secondly, let me guide you through the whole R&D; process.
To discover (not to invent) the fundamental universal principle of what exponential smoothing really IS, it required the review of the whole concept, understanding that many things don't add up and don't make much sense in currently available mainstream info, and building it all from the beginning while avoiding these very basic logical & implementation flaws.
Given a complete time t, and yet, always growing time series population that can't be logically separated into subpopulations, the very first question is, 'What amount of data do we need to utilize at time t?'. Two answers: 1 and all. You can't really gain much info from 1 datum, so go for the second answer: we need the whole dataset.
So, given the sequential & incremental nature of time series, the very first and basic thing we can do on the whole dataset is to calculate a cumulative , such as cumulative moving mean or cumulative moving median.
Now we need to extend this logic to exponential smoothing, which doesn't use dataset length info directly, but all cool it can be done via a formula that quantifies the relationship between alpha (smoothing parameter) and length. The popular formulas used in mainstream are:
alpha = 1 / length
alpha = 2 / (length + 1)
The funny part starts when you realize that Cumulative Exponential Moving Averages with these 2 alpha formulas Exactly match Cumulative Moving Average and Cumulative (Linearly) Weighted Moving Average, and the same logic goes on:
alpha = 3 / (length + 1.5) , matches Cumulative Weighted Moving Average with quadratic weights, and
alpha = 4 / (length + 2) , matches Cumulative Weighted Moving Average with cubic weghts, and so on...
It all just cries in your shoulder that we need to discover another, native length->alpha formula that leverages the recursive nature of exponential smoothing, because otherwise, it doesn't make sense to use it at all, since the usual CMA and CMWA can be computed incrementally at O(1) algo complexity just as exponential smoothing.
From now on I will not mention 'cumulative' or 'linearly weighted / weighted' anymore, it's gonna be implied all the time unless stated otherwise.
What we can do is to approach the thing logically and model the response with a little help from synthetic data, a sine wave would suffice. Then we can think of relationships: Based on algo complexity from lower to higher, we have this sequence: exponential smoothing @ O(1) -> parametric statistics (mean) @ O(n) -> non-parametric statistics (50th percentile / median) @ O(n log n). Based on Initial response from slow to fast: mean -> median Based on convergence with the real expected value from slow to fast: mean (infinitely approaches it) -> median (gets it quite fast).
Based on these inputs, we need to discover such a length->alpha formula so the resulting fit will have the slowest initial response out of all 3, and have the slowest convergence with expected value out of all 3. In order to do it, we need to have some non-linear transformer in our formula (like a square root) and a couple of factors to modify the response the way we need. I ended up with this formula to meet all our requirements:
alpha = sqrt(1 / length * 2) / 2
which simplifies to:
alpha = 1 / sqrt(len * 8)
^^ as you can see on the screenshot; where the red line is median, the blue line is the mean, and the purple line is exponential smoothing with the formulas you've just seen, we've met all the requirements.
Now we just have to do the same procedure to discover the length->alpha formula but for double exponential smoothing, which models trends as well, not just level as in single exponential smoothing. For this comparison, we need to use linear regression and quantile regression instead of the mean and median.
Quantile regression requires a non-closed form solution to be solved that you can't really implement in Pine Script, but that's ok, so I made the tests using Python & sklearn:
paste.pics
^^ on this screenshot, you can see the same relationship as on the previous screenshot, but now between the responses of quantile regression & linear regression.
I followed the same logic as before for designing alpha for double exponential smoothing (also considered the initial overshoots, but that's a little detail), and ended up with this formula:
alpha = sqrt(1 / length) / 2
which simplifies to:
alpha = 1 / sqrt(len * 4)
Btw, given the pattern you see in the resulting formulas for single and double exponential smoothing, if you ever want to do triple (not Holt & Winters) exponential smoothing, you'll need len * 2 , and just len * 1 for quadruple exponential smoothing. I hope that based on this sequence, you see the hint that Maybe 4 rounds is enough.
Now since we've dealt with the length->alpha formula, we can deal with the adaptivity part.
Logically, it doesn't make sense to use a slower-than-O(1) method to generate input for an O(1) method, so it must be something universal and minimalistic: something that will help us measure consistency in our data, yet something far away from statistics and close enough to topology.
There's one perfect entity that can help us, this is fractal efficiency. The way I define fractal efficiency can be checked at the very beginning of the post, what matters is that I add a square root to the formula that is not typically added.
As explained in the description of my metric QSFS , one of the reasons for SQRT-transformed values of fractal efficiency applied in moving window mode is because they start to closely resemble normal distribution, yet with support of (0, 1). Data with this interesting property (normally distributed yet with finite support) can be modeled with the beta distribution.
Another reason is, in infinitely expanding window mode, fractal efficiency of every time series that exhibits randomness tends to infinitely approach zero, sqrt-transform kind of partially neutralizes this effect.
Yet another reason is, the square root might better reflect the dimensional inefficiency or degree of fractal complexity, since it could balance the influence of extreme deviations from the net paths.
And finally, fractals exhibit power-law scaling -> measures like length, area, or volume scale in a non-linear way. Adding a square root acknowledges this intrinsic property, while connecting our metric with the nature of fractals.
---
I suspect that, given analogies and connections with other topics in geometry, topology, fractals and most importantly positive test results of the metric, it might be that the sqrt transform is the fundamental part of fractal efficiency that should be applied by default.
Now the last part of the ballet is to convert our fractal efficiency to length value. The part about inverse proportionality is obvious: high fractal efficiency aka high consistency -> lower window size, to utilize only the last data that contain brand new information that seems to be highly reliable since we have consistency in the first place.
The non-obvious part is now we need to neutralize the side effect created by previous sqrt transform: our length values are too low, and exponentiation is the perfect candidate to fix it since translating fractal efficiency into window sizes requires something non-linear to reflect the fractal dynamics. More importantly, using exp() was the last piece that let the metric shine, any other transformations & formulas alike I've tried always had some weird results on certain data.
That exp() in the len formula was the last piece that made it all work both on synthetic and on real data.
^^ a standalone script calculating optimal dynamic window size
Omg, THAT took time to write. Comment and/or text me if you need
...
"Versace Pip-Boy, I'm a young gun coming up with no bankroll" 👻
∞
Gold Friday Anomaly StrategyThis script implements the " Gold Friday Anomaly Strategy ," a well-known historical trading strategy that leverages the gold market's behavior from Thursday evening to Friday close. It is a backtesting-focused strategy designed to assess the historical performance of this pattern. Traders use this anomaly as it captures a recurring market tendency observed over the years.
What It Does:
Entry Condition: The strategy enters a long position at the beginning of the Friday trading session (Thursday evening close) within the defined backtesting period.
Exit Condition: Friday evening close.
Backtesting Controls: Allows users to set custom backtesting periods to evaluate strategy performance over specific date ranges.
Key Features:
Custom Backtest Periods: Easily configurable inputs to set the start and end date of the backtesting range.
Fixed Slippage and Commission Settings: Ensures realistic simulation of trading conditions.
Process Orders on Close: Backtesting is optimized by processing orders at the bar's close.
Important Notes:
Backtesting Only: This script is intended purely for backtesting purposes. Past performance is not indicative of future results.
Live Trading Recommendations: For live trading, it is highly recommended to use limit orders instead of market orders, especially during evening sessions, as market order slippage can be significant.
Default Settings:
Entry size: 10% of equity per trade.
Slippage: 1 tick.
Commission: 0.05% per trade.
Triple CCI Strategy MFI Confirmed [Skyrexio]Overview
Triple CCI Strategy MFI Confirmed leverages 3 different periods Commodity Channel Index (CCI) indicator in conjunction Money Flow Index (MFI) and Exponential Moving Average (EMA) to obtain the high probability setups. Fast period CCI is used for having the high probability to enter in the direction of short term trend, middle and slow period CCI are used for confirmation, if market now likely in the mid and long-term uptrend. MFI is used to confirm trade with the money inflow/outflow with the high probability. EMA is used as an additional trend filter. Moreover, strategy uses exponential moving average (EMA) to trail the price when it reaches the specific level. More information in "Methodology" and "Justification of Methodology" paragraphs. The strategy opens only long trades.
Unique Features
Dynamic stop-loss system: Instead of fixed stop-loss level strategy utilizes average true range (ATR) multiplied by user given number subtracted from the position entry price as a dynamic stop loss level.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Four layers trade filtering system: Strategy utilizes two different period CCI indicators, MFI and EMA indicators to confirm the signals produced by fast period CCI.
Trailing take profit level: After reaching the trailing profit activation level scrip activate the trailing of long trade using EMA. More information in methodology.
Methodology
The strategy opens long trade when the following price met the conditions:
Fast period CCI shall crossover the zero-line.
Slow and Middle period CCI shall be above zero-lines.
Price shall close above the EMA. Crossover is not obligatory
MFI shall be above 50
When long trade is executed, strategy set the stop-loss level at the price ATR multiplied by user-given value below the entry price. This level is recalculated on every next candle close, adjusting to the current market volatility.
At the same time strategy set up the trailing stop validation level. When the price crosses the level equals entry price plus ATR multiplied by user-given value script starts to trail the price with EMA. If price closes below EMA long trade is closed. When the trailing starts, script prints the label “Trailing Activated”.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 1.75)
ATR Trailing Profit Activation Level (by default = 2.25)
CCI Fast Length (by default = 14, used for calculation short term period CCI)
CCI Middle Length (by default = 25, used for calculation short term period CCI)
CCI Slow Length (by default = 50, used for calculation long term period CCI)
MFI Length (by default = 14, used for calculation MFI
EMA Length (by default = 50, period of EMA, used for trend filtering EMA calculation)
Trailing EMA Length (by default = 20)
User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Before understanding why this particular combination of indicator has been chosen let's briefly explain what is CCI, MFI and EMA.
The Commodity Channel Index (CCI) is a momentum-based technical indicator that measures the deviation of a security's price from its average price over a specific period. It helps traders identify overbought or oversold conditions and potential trend reversals.
The CCI formula is:
CCI = (Typical Price − SMA) / (0.015 × Mean Deviation)
Typical Price (TP): This is calculated as the average of the high, low, and closing prices for the period.
Simple Moving Average (SMA): This is the average of the Typical Prices over a specific number of periods.
Mean Deviation: This is the average of the absolute differences between the Typical Price and the SMA.
The result is a value that typically fluctuates between +100 and -100, though it is not bounded and can go higher or lower depending on the price movement.
The Money Flow Index (MFI) is a technical indicator that measures the strength of money flowing into and out of a security. It combines price and volume data to assess buying and selling pressure and is often used to identify overbought or oversold conditions. The formula for MFI involves several steps:
1. Calculate the Typical Price (TP):
TP = (high + low + close) / 3
2. Calculate the Raw Money Flow (RMF):
Raw Money Flow = TP × Volume
3. Determine Positive and Negative Money Flow:
If the current TP is greater than the previous TP, it's Positive Money Flow.
If the current TP is less than the previous TP, it's Negative Money Flow.
4. Calculate the Money Flow Ratio (MFR):
Money Flow Ratio = Sum of Positive Money Flow (over n periods) / Sum of Negative Money Flow (over n periods)
5. Calculate the Money Flow Index (MFI):
MFI = 100 − (100 / (1 + Money Flow Ratio))
MFI above 80 can be considered as overbought, below 20 - oversold.
The Exponential Moving Average (EMA) is a type of moving average that places greater weight and significance on the most recent data points. It is widely used in technical analysis to smooth price data and identify trends more quickly than the Simple Moving Average (SMA).
Formula:
1. Calculate the multiplier
Multiplier = 2 / (n + 1) , Where n is the number of periods.
2. EMA Calculation
EMA = (Current Price) × Multiplier + (Previous EMA) × (1 − Multiplier)
This strategy leverages Fast period CCI, which shall break the zero line to the upside to say that probability of short term trend change to the upside increased. This zero line crossover shall be confirmed by the Middle and Slow periods CCI Indicators. At the moment of breakout these two CCIs shall be above 0, indicating that there is a high probability that price is in middle and long term uptrend. This approach increases chances to have a long trade setup in the direction of mid-term and long-term trends when the short-term trend starts to reverse to the upside.
Additionally strategy uses MFI to have a greater probability that fast CCI breakout is confirmed by this indicator. We consider the values of MFI above 50 as a higher probability that trend change from downtrend to the uptrend is real. Script opens long trades only if MFI is above 50. As you already know from the MFI description, it incorporates volume in its calculation, therefore we have another one confirmation factor.
Finally, strategy uses EMA an additional trend filter. It allows to open long trades only if price close above EMA (by default 50 period). It increases the probability of taking long trades only in the direction of the trend.
ATR is used to adjust the strategy risk management to the current market volatility. If volatility is low, we don’t need the large stop loss to understand the there is a high probability that we made a mistake opening the trade. User can setup the settings ATR Stop Loss and ATR Trailing Profit Activation Level to realize his own risk to reward preferences, but the unique feature of a strategy is that after reaching trailing profit activation level strategy is trying to follow the trend until it is likely to be finished instead of using fixed risk management settings. It allows sometimes to be involved in the large movements. It’s also important to make a note, that script uses another one EMA (by default = 20 period) as a trailing profit level.
Backtest Results
Operating window: Date range of backtests is 2022.04.01 - 2024.11.25. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 50%
Maximum Single Position Loss: -4.13%
Maximum Single Profit: +19.66%
Net Profit: +5421.21 USDT (+54.21%)
Total Trades: 108 (44.44% win rate)
Profit Factor: 2.006
Maximum Accumulated Loss: 777.40 USDT (-7.77%)
Average Profit per Trade: 50.20 USDT (+0.85%)
Average Trade Duration: 44 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 2h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
US/JP Factor/Sector Performance RankingThis indicator is designed to help you easily understand the strengths and weaknesses of different factors and sectors in the U.S. stock market. It looks at various ETFs, ranks their performance over a specific period (20 days by default), and shows the results visually.
= How the Ranking Works
The best-performing rank is shown as -1, with lower ranks as -2, -3, -4, and so on. This setup makes it easy to see rank order in TradingView’s default view.
If you turn on the “Inverse” setting, ranks will be shown as positive numbers in order (e.g., 1, 2, 3…). In this case, it’s recommended to reverse the TradingView scale for better understanding.
= How the Indicator Reacts to Market Conditions
- Normal Market Conditions
Certain factors or sectors often stay at the top rank. For example, during the rallies at the start of 2024 and in May, the Momentum factor performed well, showing a risk-on market environment.
On the other hand, sectors at the bottom rank also tend to stay in specific positions.
- Market Tops
Capital flows within sectors slow down, and top ranks begin to change frequently. This may suggest a market turning point.
- Bear Markets or High Volatility
Rankings become more chaotic in these conditions. These large changes can help you understand market sentiment and the level of volatility.
= Way of using the Indicator
You can use this indicator in the following ways:
- To apply sector rotation strategies.
- To build positions after volatile markets calm down.
- To take long positions on strong elements (higher ranks) and short positions on weaker ones (lower ranks).
= Things to Keep in Mind
It’s a Lagging Indicator
This indicator calculates rankings using the past 20 days of data. It doesn’t provide signals for the future but is a tool for analyzing past performance. To predict the market, you should combine this with other tools or leading indicators.
However, since trends in capital flows often continue, this indicator can help you spot those trends.
= Customization
This indicator is set up for U.S. and Japanese stock markets. However, you can customize it for other markets by changing the ticker and label description in the script.
==Japanese Description==
このインジケーターは、米国株市場におけるファクターやセクターの強弱を直感的に把握するために設計されています。
各ETFを参照し、特定期間（デフォルトでは20日間）のパフォーマンスを順位付けし、それを視覚的に表示します。
= インジケーターの特徴
- ランク付けの仕様
ランク1位は-1で表され、順位が下がるごとに-2、-3、-4…と減少します。この仕様により、TradingViewの標準状態でランクの高低を直感的に把握できるようにしました。
さらに、Inverse設定をONにすると、1位から順に正の値（例: 1, 2, 3…）で表示されるようになります。この場合、TradingViewのスケールを反転させることを推奨します。
= 市況とインジケーターの動き
- 平常時の市況
特定のファクターやセクターがランク1位を維持することが多いです。
例えば、2024年の年初や同年5月の上昇相場では、Momentumファクターが効果を発揮し、リスクオンの市場環境であったことを示しています。
一方、最下位に位置するセクターも特定の順位を維持する傾向があります。
- 天井圏の市況
セクター内の資金流入や流出が停滞し、上位ランクの変動が起こり始めます。これが市場の転換点を示唆する場合があります。
- 下落相場や荒れた市況
ランク順位が大きく乱れることが特徴です。この変動の大きさは、市況の雰囲気やボラティリティの高さを感じ取る材料として活用できます。
= 活用方法
このインジケーターは以下のような投資戦略に役立てることができます：
- セクターローテーションを活用した投資戦略
- 荒れた相場が落ち着いたタイミングでのポジション構築
- 強い要素（ランク上位）のロング、弱い要素（ランク下位）のショート
= 注意点
- 遅行指標であること
本インジケーターは、過去20日間のデータを基にランクを算出します。そのため、先行的なシグナルを提供するものではなく、過去のパフォーマンスに基づいた分析ツールです。市場を先回りするには、別途先行指標や分析を組み合わせる必要があります。
ただし、特定のファクターやセクターへの資金流入・流出が継続する傾向があるため、これを見極める手助けにはなります。
= カスタマイズについて
このインジケーターは米国・日本株市場に特化しています。ただし、他国のファクターやセクターのETFや指数が利用可能であれば、スクリプト内のtickerとlabel descriptionを変更することでカスタマイズが可能です。
Supertrend StrategyThe Supertrend Strategy was created based on the Supertrend and Relative Strength Index (RSI) indicators, widely respected tools in technical analysis. This strategy combines these two indicators to capture market trends with precision and reliability, looking for optimizing exit levels at oversold or overbought price levels.
The Supertrend indicator identifies trend direction based on price and volatility by using the Average True Range (ATR). The ATR measures market volatility by calculating the average range between an asset’s high and low prices over a set period. It provides insight into price fluctuations, with higher ATR values indicating increased volatility and lower values suggesting stability. The Supertrend Indicator plots a line above or below the price, signaling potential buy or sell opportunities: when the price closes above the Supertrend line, an uptrend is indicated, while a close below the line suggests a downtrend. This line shifts as price movements and volatility levels change, acting as both a trailing stop loss and trend confirmation.
To enhance the Supertrend strategy, the Relative Strength Index (RSI) has been added as an exit criterion. As a momentum oscillator, the RSI indicates overbought (usually above 70) or oversold (usually below 30) conditions. This integration allows trades to close when the asset is overbought or oversold, capturing gains before a possible reversal, even if the percentage take profit level has not been reached. This mechanism aims to prevent losses due to market reversals before the Supertrend signal changes.
### Key Features
1. **Entry criteria**:
- The strategy uses the Supertrend indicator calculated by adding or subtracting a multiple of the ATR from the closing price, depending on the trend direction.
- When the price crosses above the Supertrend line, the strategy signals a long (buy) entry. Conversely, when the price crosses below, it signals a short (sell) entry.
- The strategy performs a reversal if there is an open position and a change in the direction of the supertrend occurs
2. **Exit criteria**:
- Take profit of 30% (default) on the average position price.
- Oversold (≤ 5) or overbought (≥ 95) RSI
- Reversal when there is a change in direction of the Supertrend
3. **No Repainting**:
- This strategy is not subject to repainting, as long as the timeframe configured on your chart is the same as the supertrend timeframe .
4. **Position Sizing by Equity and risk management**:
- This strategy has a default configuration to operate with 35% of the equity. At the time of opening the position, the supertrend line is typically positioned at about 12 to 16% of the entry price. This way, the strategy is putting at risk about 16% of 35% of equity, that is, around 5.6% of equity for each trade. The percentage of equity can be adjusted by the user according to their risk management.
5. **Backtest results**:
- This strategy was subjected to deep backtesting and operations in replay mode, including transaction fees of 0.12%, and slippage of 5 ticks.
- The past results in deep backtest and replay mode were compatible and profitable (Variable results depending on the take profit used, supertrend and RSI parameters). However, it should be noted that few operations were evaluated, since the currency in question has been created for a short time and the frequency of operations is relatively small.
- Past results are no guarantee of future results. The strategy's backtest results may even be due to overfitting with past data.
Default Settings
Chart timeframe: 2h
Supertrend Factor: 3.42
ATR period: 14
Supertrend timeframe: 2 h
RSI timeframe: 15 min
RSI Lenght: 5 min
RSI Upper limit: 95
RSI Lower Limit: 5
Take Profit: 30%
BYBIT:1000000MOGUSDT.P
(Early Test) Weekly Seasonality with Dynamic Kelly Criterion# Enhancing Trading Strategies with the Weekly Seasonality Dynamic Kelly Criterion Indicator
Amidst this pursuit to chase price, a common pitfall emerges: an overemphasis on price movements without adequate attention to risk management, probabilistic analysis, and strategic position sizing. To address these challenges, I developed the **Weekly Seasonality with Dynamic Kelly Criterion Indicator**. It is designed to refocus traders on essential aspects of trading, such as risk management and probabilistic returns, thereby catering to both short-term swing traders and long-term investors aiming for tax-efficient positions.
## The Motivation Behind the Indicator
### Overemphasis on Price: A Common Trading Pitfall
Many traders concentrate heavily on price charts and technical indicators, often neglecting the underlying principles of risk management and probabilistic analysis. This overemphasis on price can lead to:
- **Overtrading:** Making frequent trades based solely on price movements without considering the associated risks.
- **Poor Risk Management:** Failing to set appropriate stop-loss levels or position sizes, increasing the potential for significant losses.
- **Emotional Trading:** Letting emotions drive trading decisions rather than objective analysis, which can result in impulsive and irrational trades.
### The Need for Balanced Focus
To achieve sustained trading success, it is crucial to balance price analysis with robust risk management and probabilistic strategies. Key areas of focus include:
1. **Risk Management:** Implementing strategies to protect capital, such as setting stop-loss orders and determining appropriate position sizes based on risk tolerance.
2. **Probabilistic Analysis:** Assessing the likelihood of various market outcomes to make informed trading decisions.
3. **Swing Trading Percent Returns:** Capitalizing on short- to medium-term price movements by buying assets below their average return and selling them above.
## Introducing the Weekly Seasonality with Dynamic Kelly Criterion Indicator
The **Weekly Seasonality with Dynamic Kelly Criterion Indicator** is designed to integrate these essential elements into a comprehensive tool that aids traders in making informed, risk-aware decisions. Below, we explore the key components and functionalities of this indicator.
### Key Components of the Indicator
1. **Average Return (%)**
- **Definition:** The mean percentage return for each week across multiple years.
- **Purpose:** Serves as a benchmark to identify weeks with above or below-average performance, guiding buy and sell decisions.
2. **Positive Percentage (%)**
- **Definition:** The proportion of weeks that yielded positive returns.
- **Purpose:** Indicates the consistency of positive returns, helping traders gauge the reliability of certain weeks for trading.
3. **Volatility (%)**
- **Definition:** The standard deviation of weekly returns.
- **Purpose:** Measures the variability of returns, providing insights into the risk associated with trading during specific weeks.
4. **Kelly Ratio**
- **Definition:** A mathematical formula used to determine the optimal size of a series of bets to maximize the logarithmic growth of capital.
- **Purpose:** Balances potential returns against risks, guiding traders on the appropriate position size to take.
5. **Adjusted Kelly Fraction**
- **Definition:** The Kelly Ratio adjusted based on user-defined risk tolerance and external factors like Federal Reserve (Fed) stance.
- **Purpose:** Personalizes the Kelly Criterion to align with individual risk preferences and market conditions, enhancing risk management.
6. **Position Size ($)**
- **Definition:** The calculated amount to invest based on the Adjusted Kelly Fraction.
- **Purpose:** Ensures that position sizes are aligned with risk management strategies, preventing overexposure to any single trade.
7. **Max Drawdown (%)**
- **Definition:** The maximum observed loss from a peak to a trough of a portfolio, before a new peak is attained.
- **Purpose:** Assesses the worst-case scenario for losses, crucial for understanding potential capital erosion.
### Functionality and Benefits
- **Weekly Data Aggregation:** Aggregates weekly returns across multiple years to provide a robust statistical foundation for decision-making.
- **Quarterly Filtering:** Allows users to filter weeks based on quarters, enabling seasonality analysis and tailored strategies aligned with specific timeframes.
- **Dynamic Risk Adjustment:** Incorporates the Dynamic Kelly Criterion to adjust position sizes in real-time based on changing risk profiles and market conditions.
- **User-Friendly Visualization:** Presents all essential metrics in an organized Summary Table, facilitating quick and informed decision-making.
## The Origin of the Kelly Criterion and Addressing Its Limitations
### Understanding the Kelly Criterion
The Kelly Criterion, developed by John L. Kelly Jr. in 1956, is a formula used to determine the optimal size of a series of bets to maximize the long-term growth of capital. The formula considers both the probability of winning and the payout ratio, balancing potential returns against the risk of loss.
**Kelly Formula:**
\
Where:
- \( b \) = the net odds received on the wager ("b to 1")
- \( p \) = probability of winning
- \( q \) = probability of losing ( \( q = 1 - p \) )
### The Risk of Ruin
While the Kelly Criterion is effective in optimizing growth, it carries inherent risks:
- **Overbetting:** If the input probabilities or payout ratios are misestimated, the Kelly Criterion can suggest overly aggressive position sizes, leading to significant losses.
- **Assumption of Constant Probabilities:** The criterion assumes that probabilities remain constant, which is rarely the case in dynamic markets.
- **Ignoring External Factors:** Traditional Kelly implementations do not account for external factors such as Federal Reserve rates, margin requirements, or market volatility, which can impact risk and returns.
### Addressing Traditional Limitations
Recognizing these limitations, the **Weekly Seasonality with Dynamic Kelly Criterion Indicator** introduces enhancements to the traditional Kelly approach:
- **Incorporation of Fed Stance:** Adjusts the Kelly Fraction based on the current stance of the Federal Reserve (neutral, dovish, or hawkish), reflecting broader economic conditions that influence market behavior.
- **Margin and Leverage Considerations:** Accounts for margin rates and leverage, ensuring that position sizes remain within manageable risk parameters.
- **Dynamic Adjustments:** Continuously updates position sizes based on real-time risk assessments and probabilistic analyses, mitigating the risk of ruin associated with static Kelly implementations.
## How the Indicator Aids Traders
### For Short-Term Swing Traders
Short-term swing traders thrive on capitalizing over weekly price movements. The indicator aids them by:
- **Identifying Favorable Weeks:** Highlights weeks with above-average returns and favorable volatility, guiding entry and exit points.
- **Optimal Position Sizing:** Utilizes the Adjusted Kelly Fraction to determine the optimal amount to invest, balancing potential returns with risk exposure.
- **Probabilistic Insights:** Provides metrics like Positive Percentage (%) and Kelly Ratio to assess the likelihood of favorable outcomes, enhancing decision-making.
### For Long-Term Tax-Free Investors
This is effectively a drop-in replacement for DCA which uses fixed position size that doesn't change based on market conditions, as a result, it's like catching multiple falling knifes by the blade and smiling with blood on your hand... I don't know about you, but I'd rather juggle by the hilt and look like an actual professional...
Long-term investors, especially those seeking tax-free positions (e.g., through retirement accounts), benefit from:
- **Consistent Risk Management:** Ensures that position sizes are aligned with long-term capital preservation strategies.
- **Seasonality Analysis:** Allows for strategic positioning based on historical performance trends across different weeks and quarters.
- **Dynamic Adjustments:** Adapts to changing market conditions, maintaining optimal risk profiles over extended investment horizons.
### Developers
Please double check the logic and functionality because I think there are a few issue and I need to crowd source solutions and be responsible about the code I publish. If you have corrections, please DM me or leave a respectful comment.
I want to publish this by the end of the year and include other things like highlighting triple witching weeks, adding columns for volume % stats, VaR and CVaR, alpha, beta (to see the seasonal alpha and beta based off a benchmark ticker and risk free rate ticker and other little goodies.
TASC 2024.10 Adaptive Oscillator Threshold█ OVERVIEW
This script introduces a more dynamic approach to generating trading signals using the RSI indicator and a threshold that adapts to price trends and dispersion. This methodology comes from Francesco Bufi's article "Overbought/Oversold Oscillators: Useless Or Just Misused" from the October 2024 edition of TASC's Traders' Tips .
█ CONCEPTS
According to Francesco Bufi's observations, an oscillator-based buy signal should have a threshold that varies with the trend direction: higher during uptrends and lower during downtrends. Additionally, the level should decrease as the distance from the price to its mean increases to reduce signals in volatile conditions. Accordingly, Bufi proposes a formula for an adaptive buy level whose value is proportional to the trend (linear regression slope) and inversely proportional to the typical distance between price and its mean (standard deviation). Traders can apply this method to any oscillator to add adaptivity without modifying the oscillator's calculations, as it's simply an adaptive technique for interpreting the calculated values.
This script demonstrates the application of Bufi's Adaptive Threshold (BAT) in a simple RSI-based strategy and allows users to compare its performance to the traditional fixed-threshold approach. Bufi's observations suggest that using the BAT instead of a static threshold can help improve the backtest performance of oscillator-based systems.
█ DISCLAIMER
This strategy script educates users on the trading systems outlined by the TASC article. By default, it uses 10% of equity as the order size and a slippage amount of 5 ticks. Traders should adjust these settings and the commission amount when using this script.
Futures Risk CalculatorFutures Risk Calculator Script - Description
The Futures Risk Calculator (FRC) is a comprehensive tool designed to help traders effectively manage risk when trading futures contracts. This script allows users to calculate risk/reward ratios directly on the chart by specifying their entry price and stop loss. It's an ideal tool for futures traders who want to quantify their potential losses and gains with precision, based on their trading account size and the number of contracts they trade.
What the Script Does:
1. Risk and Reward Calculation:
The script calculates your total risk in dollars and as a percentage of your account size based on the entry and stop-loss prices you input.
It also calculates two key levels where potential reward (Take Profit 1 and Take Profit 2) can be expected, helping you assess the reward-to-risk ratio for any trade.
2. Customizable Settings:
You can specify the size of your trading account (available $ for Futures trading) and the number of futures contracts you're trading. This allows for tailored risk management that reflects your exact trading conditions.
3. Live Chart Integration:
You add the script to your chart after opening a futures chart in TradingView. Simply click on the chart to set your Entry Price and Stop Loss. The script will instantly calculate and display the risk and reward levels based on the points you set.
Adjusting the entry and stop-loss points later is just as easy: drag and drop the levels directly on the chart, and the risk and reward calculations update automatically.
4. Futures Contract Support:
The script is pre-configured with a list of popular futures symbols (like ES, NQ, CL, GC, and more). If your preferred futures contract isn’t in the list, you can easily add it by modifying the script.
The script uses each symbol’s point value to ensure precise risk calculations, providing you with an accurate dollar risk and potential reward based on the specific contract you're trading.
How to Use the Script:
1. Apply the Script to a Futures Chart:
Open a futures contract chart in TradingView.
Add the Futures Risk Calculator (FRC) script as an indicator.
2. Set Entry and Stop Loss:
Upon applying the script, it will prompt you to select your entry price by clicking the chart where you plan to enter the market.
Next, click on the chart to set your stop-loss level.
The script will then calculate your total risk in dollars and as a percentage of your account size.
3. View Risk, Reward, and (Take Profit):
You can immediately see visual lines representing your entry, stop loss, and the calculated reward-to-risk ratio levels (Take Profit 1 and Take Profit 2).
If you want to adjust the entry or stop loss after plotting them, simply move the points on
the chart, and the script will recalculate everything for you.
4. Configure Account and Contracts:
In the script settings, you can enter your account size and adjust the number of contracts you are trading. These inputs allow the script to calculate risk in monetary terms and as a percentage, making it easier to manage your risk effectively.
5. Understand the Information in the Table:
Once you apply the script, a table will appear in the top-right corner of your chart, providing you with key information about your futures contract and the trade setup. Here's what each field represents:
Account Size: Displays your total account value, which you can set in the script's settings.
Future: Shows the selected futures symbol, along with key details such as its tick size and point value. This gives you a clear understanding of how much one point or tick is worth in dollar terms.
Entry Price: The exact price at which you plan to enter the trade, displayed in green.
Stop Loss Price: The price level where you plan to exit the trade if the market moves against you, shown in red.
Contracts: The number of futures contracts you are trading, which you can adjust in the settings.
Risk: Highlighted in orange, this field shows your total risk in dollars, as well as the percentage risk based on your account size. This is a crucial value to help you stay within your risk tolerance and manage your trades effectively.
RSI Trend Following StrategyOverview
The RSI Trend Following Strategy utilizes Relative Strength Index (RSI) to enter the trade for the potential trend continuation. It uses Stochastic indicator to check is the price is not in overbought territory and the MACD to measure the current price momentum. Moreover, it uses the 200-period EMA to filter the counter trend trades with the higher probability. The strategy opens only long trades.
Unique Features
Dynamic stop-loss system: Instead of fixed stop-loss level strategy utilizes average true range (ATR) multiplied by user given number subtracted from the position entry price as a dynamic stop loss level.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Two layers trade filtering system: Strategy utilizes MACD and Stochastic indicators measure the current momentum and overbought condition and use 200-period EMA to filter trades against major trend.
Trailing take profit level: After reaching the trailing profit activation level script activates the trailing of long trade using EMA. More information in methodology.
Wide opportunities for strategy optimization: Flexible strategy settings allows users to optimize the strategy entries and exits for chosen trading pair and time frame.
Methodology
The strategy opens long trade when the following price met the conditions:
RSI is above 50 level.
MACD line shall be above the signal line
Both lines of Stochastic shall be not higher than 80 (overbought territory)
Candle’s low shall be above the 200 period EMA
When long trade is executed, strategy set the stop-loss level at the price ATR multiplied by user-given value below the entry price. This level is recalculated on every next candle close, adjusting to the current market volatility.
At the same time strategy set up the trailing stop validation level. When the price crosses the level equals entry price plus ATR multiplied by user-given value script starts to trail the price with trailing EMA(by default = 20 period). If price closes below EMA long trade is closed. When the trailing starts, script prints the label “Trailing Activated”.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 1.75)
ATR Trailing Profit Activation Level (by default = 2.25)
MACD Fast Length (by default = 12, period of averaging fast MACD line)
MACD Fast Length (by default = 26, period of averaging slow MACD line)
MACD Signal Smoothing (by default = 9, period of smoothing MACD signal line)
Oscillator MA Type (by default = EMA, available options: SMA, EMA)
Signal Line MA Type (by default = EMA, available options: SMA, EMA)
RSI Length (by default = 14, period for RSI calculation)
Trailing EMA Length (by default = 20, period for EMA, which shall be broken close the trade after trailing profit activation)
Justification of Methodology
This trading strategy is designed to leverage a combination of technical indicators—Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Stochastic Oscillator, and the 200-period Exponential Moving Average (EMA)—to determine optimal entry points for long trades. Additionally, the strategy uses the Average True Range (ATR) for dynamic risk management to adapt to varying market conditions. Let's look in details for which purpose each indicator is used for and why it is used in this combination.
Relative Strength Index (RSI) is a momentum indicator used in technical analysis to measure the speed and change of price movements in a financial market. It helps traders identify whether an asset is potentially overbought (overvalued) or oversold (undervalued), which can indicate a potential reversal or continuation of the current trend.
How RSI Works? RSI tracks the strength of recent price changes. It compares the average gains and losses over a specific period (usually 14 periods) to assess the momentum of an asset. Average gain is the average of all positive price changes over the chosen period. It reflects how much the price has typically increased during upward movements. Average loss is the average of all negative price changes over the same period. It reflects how much the price has typically decreased during downward movements.
RSI calculates these average gains and losses and compares them to create a value between 0 and 100. If the RSI value is above 70, the asset is generally considered overbought, meaning it might be due for a price correction or reversal downward. Conversely, if the RSI value is below 30, the asset is considered oversold, suggesting it could be poised for an upward reversal or recovery. RSI is a useful tool for traders to determine market conditions and make informed decisions about entering or exiting trades based on the perceived strength or weakness of an asset's price movements.
This strategy uses RSI as a short-term trend approximation. If RSI crosses over 50 it means that there is a high probability of short-term trend change from downtrend to uptrend. Therefore RSI above 50 is our first trend filter to look for a long position.
The MACD (Moving Average Convergence Divergence) is a popular momentum and trend-following indicator used in technical analysis. It helps traders identify changes in the strength, direction, momentum, and duration of a trend in an asset's price.
The MACD consists of three components:
MACD Line: This is the difference between a short-term Exponential Moving Average (EMA) and a long-term EMA, typically calculated as: MACD Line = 12 period EMA − 26 period EMA
Signal Line: This is a 9-period EMA of the MACD Line, which helps to identify buy or sell signals. When the MACD Line crosses above the Signal Line, it can be a bullish signal (suggesting a buy); when it crosses below, it can be a bearish signal (suggesting a sell).
Histogram: The histogram shows the difference between the MACD Line and the Signal Line, visually representing the momentum of the trend. Positive histogram values indicate increasing bullish momentum, while negative values indicate increasing bearish momentum.
This strategy uses MACD as a second short-term trend filter. When MACD line crossed over the signal line there is a high probability that uptrend has been started. Therefore MACD line above signal line is our additional short-term trend filter. In conjunction with RSI it decreases probability of following false trend change signals.
The Stochastic Indicator is a momentum oscillator that compares a security's closing price to its price range over a specific period. It's used to identify overbought and oversold conditions. The indicator ranges from 0 to 100, with readings above 80 indicating overbought conditions and readings below 20 indicating oversold conditions.
It consists of two lines:
%K: The main line, calculated using the formula (CurrentClose−LowestLow)/(HighestHigh−LowestLow)×100 . Highest and lowest price taken for 14 periods.
%D: A smoothed moving average of %K, often used as a signal line.
This strategy uses stochastic to define the overbought conditions. The logic here is the following: we want to avoid long trades in the overbought territory, because when indicator reaches it there is a high probability that the potential move is gonna be restricted.
The 200-period EMA is a widely recognized indicator for identifying the long-term trend direction. The strategy only trades in the direction of this primary trend to increase the probability of successful trades. For instance, when the price is above the 200 EMA, only long trades are considered, aligning with the overarching trend direction.
Therefore, strategy uses combination of RSI and MACD to increase the probability that price now is in short-term uptrend, Stochastic helps to avoid the trades in the overbought (>80) territory. To increase the probability of opening long trades in the direction of a main trend and avoid local bounces we use 200 period EMA.
ATR is used to adjust the strategy risk management to the current market volatility. If volatility is low, we don’t need the large stop loss to understand the there is a high probability that we made a mistake opening the trade. User can setup the settings ATR Stop Loss and ATR Trailing Profit Activation Level to realize his own risk to reward preferences, but the unique feature of a strategy is that after reaching trailing profit activation level strategy is trying to follow the trend until it is likely to be finished instead of using fixed risk management settings. It allows sometimes to be involved in the large movements.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.08.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 30%
Maximum Single Position Loss: -3.94%
Maximum Single Profit: +15.78%
Net Profit: +1359.21 USDT (+13.59%)
Total Trades: 111 (36.04% win rate)
Profit Factor: 1.413
Maximum Accumulated Loss: 625.02 USDT (-5.85%)
Average Profit per Trade: 12.25 USDT (+0.40%)
Average Trade Duration: 40 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 2h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
