AI's Opinion Trading System V21. Complete Summary of the Indicator Script
AI’s Opinion Trading System V2 is an advanced, multi-factor trading tool designed for the TradingView platform. It combines several technical indicators (moving averages, RSI, MACD, ADX, ATR, and volume analysis) to generate buy, sell, and hold signals. The script features a customizable AI “consensus” engine that weighs multiple indicator signals, applies user-defined filters, and outputs actionable trade instructions with clear stop loss and take profit levels. The indicator also tracks sentiment, volume delta, and allows for advanced features like pyramiding (adding to positions), custom stop loss/take profit prices, and flexible signal confirmation logic. All key data and signals are displayed in a dynamic, color-coded table on the chart for easy review.
2. Full Explanation of the Table
The table is a real-time dashboard summarizing the indicator’s logic and recommendations for the most recent bars. It is color-coded for clarity and designed to help traders quickly understand market conditions and AI-driven trade signals.
Columns (from left to right):
Column Name What it Shows
Bar The time context: “Now” for the current bar, then “Bar -1”, “Bar -2”, etc. for previous bars.
Raw Consensus The raw AI consensus for each bar: “Buy”, “Sell”, or “-” (neutral).
Up Vol The amount of volume on up (rising) bars.
Down Vol The amount of volume on down (falling) bars.
Delta The difference between up and down volume. Green if positive, red if negative, gray if neutral.
Close The closing price for each bar, color-coded by price change.
Sentiment Diff The difference between the close and average sentiment price (a custom sentiment calculation).
Lookback The number of bars used for sentiment calculation (if enabled).
ADX The ADX value (trend strength).
ATR The ATR value (volatility measure).
Vol>Avg “Yes” (green) if volume is above average, “No” (red) otherwise.
Confirm Whether the AI signal is confirmed over the required bars.
Logic Output The AI’s interpreted signal after applying user-selected logic: “Buy”, “Sell”, or “-”.
Final Action The final signal after all filters: “Buy”, “Sell”, or “-”.
Trade Instruction A plain-English instruction: Buy/Sell/Add/Hold/No Action, with price, stop loss, and take profit.
Color Coding:
Green: Positive/bullish values or signals
Red: Negative/bearish values or signals
Gray: Neutral or inactive
Blue background: For all table cells, for visual clarity
White text: Default, except for color-coded cells
3. Full User Instructions for Every Input/Style Option
Below are plain-language instructions for every user-adjustable option in the indicator’s input and style pages:
Inputs
Table Location
What it does: Sets where the summary table appears on your chart.
How to use: Choose from 9 positions (Top Left, Top Center, Top Right, etc.) to avoid overlapping with other chart elements.
Decimal Places
What it does: Controls how many decimal places prices and values are displayed with.
How to use: Increase for assets with very small prices (e.g., SHIB), decrease for stocks or forex.
Show Sentiment Lookback?
What it does: Shows or hides the “Lookback” column in the table, which displays how many bars are used in the sentiment calculation.
How to use: Turn off if you want a simpler table.
AI View Mode
What it does: Selects the logic for how the AI combines signals from different indicators.
Majority: Follows the most common signal among all indicators.
Weighted: Uses custom weights for each type of signal.
Custom: Lets you define your own logic (see below).
How to use: Pick the logic style that matches your trading philosophy.
AI Consensus Weight / Vol Delta Weight / Sentiment Weight
What they do: When using “Weighted” AI View Mode, these let you set how much influence each factor (indicator consensus, volume delta, sentiment) has on the final signal.
How to use: Increase a weight to make that factor more important in the AI’s decision.
Custom AI View Logic
What it does: Lets advanced users write their own logic for when the AI should signal a trade (e.g., “ai==1 and delta>0 and sentiment>0”).
How to use: Only use if you understand basic boolean logic.
Use Custom Stop Loss/Take Profit Prices?
What it does: If enabled, you can enter your own fixed stop loss and take profit prices for buys and sells.
How to use: Turn on to override the auto-calculated SL/TP and enter your desired prices below.
Custom Buy/Sell Stop Loss/Take Profit Price
What they do: If custom SL/TP is enabled, these fields let you set exact prices for stop loss and take profit on both buy and sell trades.
How to use: Enter your preferred price, or leave at 0 for auto-calculation.
Sentiment Lookback
What it does: Sets how many bars the sentiment calculation should look back.
How to use: Increase to smooth out sentiment, decrease for faster reaction.
Max Pyramid Adds
What it does: Limits how many times you can add to an existing position (pyramiding).
How to use: Set to 1 for no adds, higher for more aggressive scaling in trends.
Signal Preset
What it does: Quick-sets a group of signal parameters (see below) for “Robust”, “Standard”, “Freedom”, or “Custom”.
How to use: Pick a preset, or select “Custom” to adjust everything manually.
Min Bars for Signal Confirmation
What it does: Sets how many bars a signal must persist before it’s considered valid.
How to use: Increase for more robust, less frequent signals; decrease for faster, but possibly less reliable, signals.
ADX Length
What it does: Sets the period for the ADX (trend strength) calculation.
How to use: Longer = smoother, shorter = more sensitive.
ADX Trend Threshold
What it does: Sets the minimum ADX value to consider a trend “strong.”
How to use: Raise for stricter trend confirmation, lower for more trades.
ATR Length
What it does: Sets the period for the ATR (volatility) calculation.
How to use: Longer = smoother volatility, shorter = more reactive.
Volume Confirmation Lookback
What it does: Sets how many bars are used to calculate the average volume.
How to use: Longer = more stable volume baseline, shorter = more sensitive.
Volume Confirmation Multiplier
What it does: Sets how much current volume must exceed average volume to be considered “high.”
How to use: Increase for stricter volume filter.
RSI Flat Min / RSI Flat Max
What they do: Define the RSI range considered “flat” (i.e., not trending).
How to use: Widen to be stricter about requiring a trend, narrow for more trades.
Style Page
Most style settings (such as plot colors, label sizes, and shapes) are preset in the script for visual clarity.
You can adjust plot visibility and colors (for signals, stop loss, take profit) in the TradingView “Style” tab as with any indicator.
Buy Signal: Shows as a green triangle below the bar when a buy is triggered.
Sell Signal: Shows as a red triangle above the bar when a sell is triggered.
Stop Loss/Take Profit Lines: Red and green lines for SL/TP, visible when a trade is active.
SL/TP Labels: Small colored markers at the SL/TP levels for each trade.
How to use:
Toggle visibility or change colors in the Style tab if you wish to match your chart theme or preferences.
In Summary
This indicator is highly customizable—you can tune every aspect of the AI logic, risk management, signal filtering, and table display to suit your trading style.
The table gives you a real-time, comprehensive view of all relevant signals, filters, and trade instructions.
All inputs are designed to be intuitive—hover over them in TradingView for tooltips, or refer to the explanations above for details.
Wyszukaj w skryptach "ai"
AI Strat ATR Dinamico + ADX + Trend Adaptivo (No Repaint)Below is a fully self-contained, English-language description of every input, function, and logical block inside the “AI Strat ATR Dinamico + ADX + Trend Adaptivo (No Repaint)” indicator. You can copy and paste this into TradingView’s “Description” field when you publish, without exposing any Pine code.
---
## Indicator Name and Purpose
**Name (Short Title):**
AI Strat Adaptive v3 (NoRepaint)
**Overview:**
This indicator combines multiple technical tools—RSI, EMA, ATR (with a dynamic multiplier), ADX/DI, and an “AI‐style” scoring mechanism—to generate trend-filtered and reversal signals. It also optionally confirms signals on a higher timeframe, dynamically adjusts its sensitivity based on volatility, and plots intrabar stop‐loss (SL) and take‐profit (TP) levels derived from ATR. Special care has been taken to ensure that no signals “repaint” (i.e., once drawn on a closed bar, they never disappear or shift).
---
## 1. Main Inputs
All of the inputs appear in the Settings dialog for the published indicator. Below is a detailed explanation of each input, grouped by logical category.
### A. RSI & EMA Base Parameters
1. **RSI Length (Base)**
* **Input type:** Integer (default 14)
* **Description:** Number of bars used to calculate the Relative Strength Index (RSI). A shorter RSI reacts more quickly to price changes; a longer RSI is smoother.
2. **RSI Overbought Threshold**
* **Input type:** Integer (default 60)
* **Description:** If the RSI value rises above this level, it contributes a “sell” signal component. You can adjust this (e.g., 70) to make your system more conservative.
3. **RSI Oversold Threshold**
* **Input type:** Integer (default 40)
* **Description:** If the RSI falls below this level, it contributes a “buy” signal component. Raising this threshold (e.g., 50) makes the strategy more aggressive in seeking reversals.
4. **EMA Length (Base)**
* **Input type:** Integer (default 20)
* **Description:** Number of bars for the Exponential Moving Average (EMA). A shorter EMA will produce more frequent crossovers, a longer EMA is smoother.
### B. ATR & Volatility Filter Parameters
5. **ATR Length (Base)**
* **Input type:** Integer (default 14)
* **Description:** Number of bars to calculate Average True Range (ATR). The ATR is used both for measuring volatility and for dynamic SL/TP levels.
6. **ATR SMA Length**
* **Input type:** Integer (default 50)
* **Description:** Number of bars to compute a Simple Moving Average of the ATR itself. This gives a baseline of “normal” volatility. If ATR rises significantly above this SMA, the indicator treats the market as “high volatility.”
7. **ATR Multiplier Base**
* **Input type:** Float (default 1.2, step 0.1)
* **Description:** Base multiplier for ATR when filtering for volatility. The actual threshold is computed as `ATR_SMA × (ATR_Multiplier Base) × sqrt(current_ATR / ATR_SMA)`. In other words, the multiplier becomes larger if volatility is rising, and smaller if volatility is falling.
8. **Disable Volatility Filter**
* **Input type:** Boolean (default false)
* **Description:** If enabled (true), the indicator will ignore any volatility‐based filtering, using signals regardless of ATR behavior. If disabled (false), signals only fire when ATR > (ATR\_SMA × dynamic multiplier).
### C. Price-Change & “AI Score” Parameters
9. **Price Change Period (bars)**
* **Input type:** Integer (default 3)
* **Description:** The number of bars back to measure percentage price change. Used to ensure that a “trend” signal is accompanied by a sufficiently positive (for longs) or negative (for shorts) price movement over this many bars.
10. **Base AI Score Threshold**
* **Input type:** Float (default 0.1)
* **Description:** The indicator computes a composite “AI-style” score by combining the RSI signal (overbought/oversold) and an EMA crossover signal. Only if the absolute value of that composite score exceeds this threshold will a trend signal be eligible. Raising it makes signals rarer but (potentially) higher-conviction.
### D. SMA “ICT” Trend Filter Parameters
11. **ICT SMA Long Length (Base)**
* **Input type:** Integer (default 50)
* **Description:** Number of bars for the “long” Simple Moving Average (SMA) used in the internal trend filter. Typically, price must be above this SMA (and ADX must be strong) to confirm an uptrend, or below it (and ADX strong) to confirm a downtrend.
12. **ICT SMA Short1 Length (Base)**
* **Input type:** Integer (default 10)
* **Description:** Secondary “fast” SMA used both for reversal logic (e.g., price crossing above it can count as a bullish reversal) and part of the internal trend confirmation.
13. **ICT SMA Short2 Length (Base)**
* **Input type:** Integer (default 20)
* **Description:** A second “medium” SMA used for reversal triggers (e.g., crossovers or crossunders alongside RSI conditions).
### E. ADX & DI Parameters
14. **Base ADX Length**
* **Input type:** Integer (default 14)
* **Description:** Number of bars for the ADX (Average Directional Index) moving averages, which measure trend strength. The same length is used for +DI and –DI smoothing.
15. **Base ADX Threshold**
* **Input type:** Float (default 25.0, step 0.5)
* **Description:** If ADX > this threshold and +DI > –DI, we consider an uptrend; if ADX > this threshold and –DI > +DI, we consider a downtrend. Raising this value demands stronger trends to qualify.
### F. Sensitivity & Cooldown
16. **Sensitivity (0–1)**
* **Input type:** Float between 0.0 and 1.0 (default 0.5)
* **Description:** A general “mixture” parameter used internally to weight how aggressively the indicator leans into trend versus reversal. In practice, the code uses it to fine-tune exact thresholds for switching between trend and reversal conditions. You can leave it at 0.5 unless you want to bias more heavily toward either regime.
17. **Base Cooldown Bars Between Signals**
* **Input type:** Integer (default 5, min 0)
* **Description:** Once a long or short signal fires, the indicator will wait at least this many bars before allowing a new signal in the same direction. Prevents “signal flipping” on each bar. A higher number forces fewer, more spaced-out entries.
18. **Trend Confirmation Bars**
* **Input type:** Integer (default 3, min 1)
* **Description:** After the directional filters (+DI/–DI cross, price vs. SMA), the indicator still requires that price remains on the same side of the long SMA for at least this many consecutive bars before confirming “trend up” or “trend down.” Larger values smooth out false breakouts but may lag signals.
### G. Higher Timeframe Confirmation
19. **Use Higher Timeframe Confirmation**
* **Input type:** Boolean (default true)
* **Description:** If true, the indicator will request a block of values (SMA, +DI, –DI, ADX) from a higher timeframe (default 60 minutes) and require that the higher timeframe is also in agreement (strong uptrend or strong downtrend) before confirming your current-timeframe trend. This helps filter out lower-timeframe noise.
20. **Higher Timeframe (TF) for Confirmation**
* **Input type:** Timeframe (default “60”)
* **Description:** The chart timeframe (e.g., 5, 15, 60 minutes) whose trend conditions must also be true. It’s sent through a `request.security(..., lookahead=barmerge.lookahead_off)` call so that it never “paints ahead.”
### H. Dynamic TP/SL Parameters
21. **TP as ATR Multiple**
* **Input type:** Float (default 2.0, step 0.1)
* **Description:** When a trade is open, the “take-profit” price is determined by looking at the highest high (for longs) or lowest low (for shorts) observed since entry, and then plotting a cross (“X”) at that level when the trend finally flips. This is purely for display. However, separate from that, this parameter can be adapted if you want a strictly ATR–based TP. In the “Minimal” version, TP is ≈ (highest high) once trend inverts, but you could rewrite it to use `entry_price + ATR×TP_Multiplier`.
22. **SL as ATR Multiple**
* **Input type:** Float (default 1.0, step 0.1)
* **Description:** While in a trade, a trailing SL line is plotted each bar. Its value is always `entry_price ± (ATR × SL_Multiplier)`. When the trend inverts, the SL no longer updates, and you see it on the chart.
### I. Display and Mode Options
23. **Show Debug Lines**
* **Input type:** Boolean (default true)
* **Description:** When enabled, the indicator will plot all intermediate lines—ATR SMA, ATR Threshold, +DI, –DI, ADX (current and HTF), HTF SMA, etc.—so that you can diagnose exactly what’s happening. Turn this off to hide all debug information and only see entry/exit shapes.
24. **Enable Scalping Mode**
* **Input type:** Boolean (default false)
* **Description:** If true, many of the “base” parameters are halved (e.g., RSI length becomes 7 instead of 14, ATR length becomes 7 instead of 14, ADX length becomes 7, etc.), and the ADX threshold is multiplied by 0.8. This makes all oscillators and moving averages more reactive, suited for very short-term (scalping) setups.
---
## 2. Core Calculation Blocks
Below is a high-level description of each logical block (in code order), translated from Pine into conceptual steps.
### A. Adjust Inputs if “Scalping Mode” Is On
If **Scalping Mode** = true, then:
* `RSI_Length` becomes `max(1, round(Base_RSI_Length / 2))`
* `EMA_Length` becomes `max(1, round(Base_EMA_Length / 2))`
* `ATR_Length` becomes `max(1, round(Base_ATR_Length / 2))`
* `Price_Change_Period` becomes `max(1, round(Base_Price_Change_Period / 2))`
* `SMA_Long_Length`, `SMA_Short1_Length`, and `SMA_Short2_Length` are each halved (minimum 1).
* `ADX_Length` = `max(1, round(Base_ADX_Length / 2))`
* `ADX_Threshold` = `Base_ADX_Threshold × 0.8`
* `Cooldown_Bars` = `max(0, round(Base_Cooldown_Bars / 2))`
Otherwise, all adjusted lengths = their base values.
### B. RSI, EMA & “AI Score” on Current Timeframe
1. **Compute RSI:**
* Uses the (possibly adjusted) `RSI_Length`.
* Denote this as `RSI_Value`.
2. **Compute ATR & Its SMA:**
* `ATR_Value` = `ta.atr(ATR_Length)`.
* `ATR_SMA` = `ta.sma(ATR_Value, ATR_SMA_Length)`.
* Then define `Volatility_Increase` = (`ATR_Value > ATR_SMA`).
* If the volatility has increased, the weighting of RSI vs. EMA changes.
3. **Compute Weights:**
* If `Volatility_Increase == true`, then:
* `RSI_Weight = 0.7`
* `EMA_Weight = 0.3`
* Otherwise:
* `RSI_Weight = 0.3`
* `EMA_Weight = 0.7`
4. **RSI Signal Component (`RSI_Sig`):**
* If `RSI_Value > RSI_Overbought`, then `RSI_Sig = –1`.
* Else if `RSI_Value < RSI_Oversold`, then `RSI_Sig = +1`.
* Otherwise, `RSI_Sig = 0`.
5. **EMA Value & Signal Component (`EMA_Sig`):**
* `EMA_Value` = `ta.ema(close, EMA_Length)`.
* `EMA_Sig = +1` if the current close crosses **above** the EMA; `EMA_Sig = –1` if the current close crosses **below** the EMA; else `0`.
6. **Compute Raw “AI Score”:**
$$
Raw\_AI = (RSI\_Sig \times RSI\_Weight)\;+\;(EMA\_Sig \times EMA\_Weight)
$$
Then,
$$
AI\_Score = \frac{Raw\_AI}{(RSI\_Weight + EMA\_Weight)}
$$
(This normalization ensures the score always ranges between –1 and +1 if both weights sum to 1.)
### C. Dynamic ATR Multiplier & Volatility Filter
1. **Volatility Factor:**
$$
Volatility\_Factor = \frac{ATR\_Value}{ATR\_SMA}
$$
2. **Dynamic ATR Multiplier:**
$$
ATR\_Multiplier = ATR\_Multiplier\_Base \times \sqrt{Volatility\_Factor}
$$
3. **High Volatility Condition (`High_Volatility`):**
* If `Disable_Volatility_Filter == true`, then treat `High_Volatility = true` always.
* Else, `High_Volatility = (ATR_Value > ATR_SMA × ATR_Multiplier)`.
### D. Price Change Percentage
* **Compute Price Change:**
$$
Price\_Change = \frac{(Close - Close )}{Close } \times 100
$$
* This is the percent return from `Price_Change_Period` bars ago to now.
* For a valid long‐trend signal, we require `Price_Change > 0`; for a short trend, `Price_Change < 0`.
### E. Local SMAs for Trend/Reversal Filters
* `SMA_Close_Long` = `ta.sma(close, SMA_Long_Length)`.
* `SMA_Close_Short1` = `ta.sma(close, SMA_Short1_Length)`.
* `SMA_Close_Short2` = `ta.sma(close, SMA_Short2_Length)`.
These three SMAs help define the “local trend” and reversal breakout points:
* **Primary Trend Filter:**
* Price must be above `SMA_Close_Long` for an uptrend filter, or below `SMA_Close_Long` for a downtrend filter.
* **Reversal Filter:**
* A bullish reversal is detected if **(RSI < Oversold AND close crosses above EMA)** OR **(RSI < Oversold AND close crosses above SMA\_Close\_Short1)**.
* A bearish reversal is detected if **(RSI > Overbought AND close crosses below EMA)** OR **(RSI > Overbought AND close crosses below SMA\_Close\_Short1)**.
### F. Manual +DI, –DI & ADX on Current Timeframe
Instead of relying on the built-in `ta.adx`, the script calculates DI and ADX manually. This makes it easier to replicate the exact logic on a higher timeframe via `request.security`. The steps are:
1. **Directional Movement (DM) Components:**
* `Up_Move` = `high – high `
* `Down_Move` = `low – low`
* `Plus_DM` = `Up_Move` if (`Up_Move > Down_Move` AND `Up_Move > 0`), else `0`
* `Minus_DM` = `Down_Move` if (`Down_Move > Up_Move` AND `Down_Move > 0`), else `0`
2. **True Range (TR) Components:**
* `TR1` = `high – low`
* `TR2` = `abs(high – close )`
* `TR3` = `abs(low – close )`
* `True_Range` = `max(TR1, TR2, TR3)`
3. **Smoothed Averages (RMA):**
* `Sm_TR` = `ta.rma(True_Range, ADX_Length)`
* `Sm_Plus` = `ta.rma(Plus_DM, ADX_Length)`
* `Sm_Minus`= `ta.rma(Minus_DM, ADX_Length)`
4. **Compute DI%:**
$$
Plus\_DI = \frac{Sm\_Plus}{Sm\_TR} \times 100,\quad
Minus\_DI = \frac{Sm\_Minus}{Sm\_TR} \times 100
$$
5. **DX and ADX:**
$$
DX = \frac{|Plus\_DI - Minus\_DI|}{Plus\_DI + Minus\_DI} \times 100,\quad
ADX = ta.rma(DX, ADX_Length)
$$
These values are referred to as `(plus_di, minus_di, adx_val)` for the current timeframe.
---
## 3. Higher Timeframe (HTF) Confirmation Function
If **Use Higher Timeframe Confirmation** is enabled, the script calls a single helper (Pine) function `f_htf` with two parameters: the ADX length and the SMA length (both taken from the “base” or “scaled” values). Internally, `f_htf` simply reruns the manual DI/ADX logic (same as above) on the higher timeframe’s bar data, and also includes that timeframe’s closing price and its SMA for trend comparison.
* **Request.Security Call:**
```
= request.security(
syminfo.tickerid,
higher_tf,
f_htf(adx_length, sma_long_len),
lookahead=barmerge.lookahead_off
)
```
* `lookahead=barmerge.lookahead_off` ensures that no HTF value “paints” early; you always see only confirmed HTF bars.
* The returned tuple provides:
1. `ht_close` = HTF closing price
2. `ht_sma` = HTF SMA of length `sma_long_len`
3. `ht_pdi` = HTF +DI percentage
4. `ht_mdi` = HTF –DI percentage
5. `ht_adx` = HTF ADX value
---
## 4. Trend & Reversal Filters (Current & HTF)
### A. Current-Timeframe Trend Filter
1. **Uptrend\_Basic (Current TF)**
$$
(plus\_di > minus\_di)\;\land\;(adx\_val > ADX\_Threshold)\;\land\;(close > SMA\_Close\_Long)
$$
2. **Downtrend\_Basic (Current TF)**
$$
(minus\_di > plus\_di)\;\land\;(adx\_val > ADX\_Threshold)\;\land\;(close < SMA\_Close\_Long)
$$
3. **Trend Confirmation by Bars:**
* `Bars_Since_Below` = number of bars since `close <= SMA_Close_Long`.
* `Bars_Since_Above` = number of bars since `close >= SMA_Close_Long`.
* If `Uptrend_Basic == true` AND `Bars_Since_Below ≥ Trend_Confirmation_Bars` → mark `Uptrend_Confirm = true`.
* If `Downtrend_Basic == true` AND `Bars_Since_Above ≥ Trend_Confirmation_Bars` → mark `Downtrend_Confirm = true`.
### B. Reversal Filters (Current TF)
1. **Bullish Reversal (`Rev_Bullish`):**
* If `(RSI < RSI_Oversold AND close crosses above EMA_Value)` OR
`(RSI < RSI_Oversold AND close crosses above SMA_Close_Short1)`
→ then `Rev_Bullish = true`.
2. **Bearish Reversal (`Rev_Bearish`):**
* If `(RSI > RSI_Overbought AND close crosses below EMA_Value)` OR
`(RSI > RSI_Overbought AND close crosses below SMA_Close_Short1)`
→ then `Rev_Bearish = true`.
### C. Higher-Timeframe Trend Filter (HTF)
1. **HTF Uptrend (`HT_Uptrend`):**
$$
(ht\_pdi > ht\_mdi)\;\land\;(ht\_adx > ADX\_Threshold)\;\land\;(ht\_close > ht\_sma)
$$
2. **HTF Downtrend (`HT_Downtrend`):**
$$
(ht\_mdi > ht\_pdi)\;\land\;(ht\_adx > ADX\_Threshold)\;\land\;(ht\_close < ht\_sma)
$$
3. **Combine Current & HTF:**
* If **Use\_HTF\_Confirmation == true**, then:
* `Uptrend_Confirm := Uptrend_Confirm AND HT_Uptrend`
* `Downtrend_Confirm := Downtrend_Confirm AND HT_Downtrend`
* Otherwise, just use the current timeframe’s `Uptrend_Confirm` and `Downtrend_Confirm`.
4. **Define `CurrentTrend` (Integer):**
* `CurrentTrend = +1` if `Uptrend_Confirm == true`.
* `CurrentTrend = –1` if `Downtrend_Confirm == true`.
* Otherwise, `CurrentTrend = 0`.
5. **Reset “One Trade Per Trend”:**
* There is a persistent variable `LastTradeTrend`.
* Every time `CurrentTrend` flips (i.e., `CurrentTrend != CurrentTrend `), the code sets `LastTradeTrend := 0`.
* That allows one new entry once the detected trend has changed.
---
## 5. One‐Time “Cooldown” Logic
* **`LastSignalBar`**
* A persistent integer (initially undefined).
* After each confirmed long or short entry, `LastSignalBar` is set to the bar index where that signal fired.
* **`Bars_Since_Signal`**
* If `LastSignalBar` is undefined, treat as a very large number (so that initial signals are always allowed).
* Otherwise, `Bars_Since_Signal = bar_index – LastSignalBar`.
* **Cooldown Check:**
* A new long (or short) can only be generated if `(Bars_Since_Signal > Signal_Cooldown)`.
* This prevents multiple signals in rapid succession.
---
## 6. Entry Conditions (No Repaint)
All of the conditions below are calculated “intrabar,” but the script only actually registers a **signal** on **bar close** (`barstate.isconfirmed`) so that signals never repaint.
### A. Trend‐Based “Raw” Conditions
1. **Trend\_Long\_Raw:**
$$
(AI\_Score > AI\_Score\_Threshold)\;\land\;Uptrend\_Confirm\;\land\;High\_Volatility\;\land\;(Price\_Change > 0)
$$
2. **Trend\_Short\_Raw:**
$$
(AI\_Score < -AI\_Score\_Threshold)\;\land\;Downtrend\_Confirm\;\land\;High\_Volatility\;\land\;(Price\_Change < 0)
$$
### B. Reversal “Raw” Conditions
1. **Rev\_Long\_Raw:**
$$
Rev\_Bullish\;\land\;(CurrentTrend \neq +1)
$$
2. **Rev\_Short\_Raw:**
$$
Rev\_Bearish\;\land\;(CurrentTrend \neq -1)
$$
### C. Combine Raw Signals
* `Raw_Long = Trend_Long_Raw OR Rev_Long_Raw`.
* `Raw_Short = Trend_Short_Raw OR Rev_Short_Raw`.
### D. Confirmed Long/Short Signal Flags
On each new bar **close** (`barstate.isconfirmed == true`):
* **Long\_Signal\_Confirmed** can fire if:
1. `Raw_Long == true`
2. `LastTradeTrend != +1` (we haven’t already taken a long in this same trend)
3. `Bars_Since_Signal > Signal_Cooldown`
If all three hold, then on this bar close the code sets:
* `Long_Signal = true`
* `LastTradeTrend := +1`
* `LastSignalBar := bar_index`
Otherwise, `Long_Signal := false` on this bar.
* **Short\_Signal\_Confirmed** works the same way but with `Raw_Short`, `LastTradeTrend != -1`, etc.
If triggered, it sets `Short_Signal = true`, `LastTradeTrend := -1`, and `LastSignalBar := bar_index`. Otherwise `Short_Signal := false`.
* **Important:** If the bar is still forming (`else` branch of `barstate.isconfirmed`), then both `Long_Signal` and `Short_Signal` are forced to `false`. This guarantees that no shape or alert appears until the bar actually closes.
---
## 7. Plotting Entry/Exit Shapes
1. **Trend Long Signal (Triangle Up)**
* Condition: `Long_Signal == true` **AND** `Trend_Long_Raw == true`.
* Appearance: A small, semi-transparent lime green triangle drawn **below** the bar.
2. **Trend Short Signal (Triangle Down)**
* Condition: `Short_Signal == true` **AND** `Trend_Short_Raw == true`.
* Appearance: A small, semi-transparent maroon triangle drawn **above** the bar.
3. **Reversal Long Signal (Circle)**
* Condition: `Long_Signal == true` **AND** `Rev_Long_Raw == true`.
* Appearance: A tiny, more transparent green circle drawn **below** the bar.
4. **Reversal Short Signal (Circle)**
* Condition: `Short_Signal == true` **AND** `Rev_Short_Raw == true`.
* Appearance: A tiny, more transparent red circle drawn **above** the bar.
Since `Long_Signal` and `Short_Signal` only ever become true at bar close, these shapes are never repainted or removed once drawn.
---
## 8. Unified Alert Message
* As soon as a new bar closes with either `Long_Signal` or `Short_Signal == true`, an alert message is sent:
* If `Long_Signal`, then `alert_msg = "action=BUY"`.
* If `Short_Signal`, then `alert_msg = "action=SELL"`.
* If neither, `alert_msg = ""` (no alert).
* The code calls `alert(alert_msg, freq=alert.freq_once_per_bar)` only if `barstate.isconfirmed` and `alert_msg` is non‐empty. This ensures exactly one alert per confirmed bar, no intrabar pops.
---
## 9. Dynamic TP/SL Logic (Minimal Implementation)
Once a long or short position is “open,” the script tracks these variables:
1. **Persistent Flags and Prices** (all persist between bars until reset):
* `InLong` (Boolean)
* `InShort` (Boolean)
* `Long_Max` (Float)
* `Short_Min` (Float)
* `Entry_Price` (Float)
2. **On Bar Close:**
* If `Long_Signal == true` →
* Set `InLong := true`,
* `Entry_Price := close` of that bar,
* `Long_Max := high ` (last bar’s high, so that we’re not using “future” data).
* If `Short_Signal == true` →
* Set `InShort := true`,
* `Entry_Price := close`,
* `Short_Min := low `.
3. **While `InLong == true`:**
* Continuously update `Long_Max = max(Long_Max, current high)` on each bar (intrabar, but finalized each close).
* Compute a dynamic SL:
$$
SL_{Long} = Entry\_Price - (ATR \times SL\_ATR\_Multiplier).
$$
* If **current trend** flips to non-uptrend (`CurrentTrend != +1`), mark `ExitLong = true`.
* Then the routine plots `TP_Long = Long_Max` as a cross (“X”) at that level.
* Set `InLong := false` so that no further changes to `Long_Max` or `Entry_Price` happen on future bars.
4. **While `InShort == true`:**
* Continuously update `Short_Min = min(Short_Min, current low)`.
* Compute a dynamic SL:
$$
SL_{Short} = Entry\_Price + (ATR \times SL\_ATR\_Multiplier).
$$
* If trend flips to non-downtrend (`CurrentTrend != –1`), mark `ExitShort = true`.
* Then the routine plots `TP_Short = Short_Min`.
* Set `InShort := false` to freeze those values.
5. **Plotting TP/SL if “Show Debug” is On:**
* **TP Shapes:**
* When `ExitLong == true`, plot a solid lime “X” at `TP_Long` (highest high).
* When `ExitShort == true`, plot a solid maroon “X” at `TP_Short` (lowest low).
* **SL Lines:**
* If still `InLong`, draw a thin red line at `SL_Long` on each bar.
* If still `InShort`, draw a thin green line at `SL_Short`.
Thus, your charts visually show the highest‐high take-profit cross for longs, the lowest-low take-profit cross for shorts, and a continuously updating trailing SL until the trend flips. Because all of this is triggered on confirmed bars, nothing “jumps around” after the fact.
---
## 10. Debug‐Only Plot Lines (When Enabled)
When **Show Debug Lines** = true, the indicator will also plot:
1. **ATR SMA (Orange):**
* The simple moving average of ATR over `ATR_SMA_Length`.
2. **ATR Threshold (Yellow):**
* `ATR_SMA × ATR_Multiplier` (the dynamically scaled threshold).
3. **+DI & –DI (Current TF):**
* +DI plotted as a green line, –DI plotted as a red line (opacity \~70%).
4. **ADX (Current TF, Blue):**
* A blue line for the present timeframe’s ADX.
5. **ADX Threshold (Gray):**
* A horizontal gray line showing `ADX_Threshold`.
6. **+DI & –DI (HTF, Darker Colors):**
* If HTF confirmation is on, “HTF +DI” is a greener but more transparent line; “HTF –DI” is a redder but more transparent line.
7. **ADX (HTF, Blue but Transparent):**
* HTF ADX plotted in blue (high transparency).
8. **HTF SMA (Orange, Transparent):**
* The higher timeframe’s SMA (same length as `SMA_Long_Length`), drawn in fainter orange.
9. **Volatility Zone Fill (Yellow Tinted Area):**
* Fills the area between `ATR_SMA` and `ATR_SMA × ATR_Multiplier`.
* Indicates “normal” versus “high‐volatility” regimes.
These debug lines are purely visual aids. Disable them if you want a cleaner chart.
---
## 11. Putting It All Together — Step-By-Step Flow
1. **Read Inputs** (RSI lengths, EMA length, ATR settings, etc.).
2. **Optionally Halve All Lengths** if “Scalping Mode” is checked.
3. **Calculate Current TF Indicators:**
* RSI, ATR, ATR\_SMA, EMA, price change, various SMAs, DI/ADX.
4. **Compute “AI Score”** (weighted sum of RSI and EMA signals).
5. **Compute Dynamic ATR Multiplier** and decide if “High Volatility” is true.
6. **Compute Raw Trend/Reversal Conditions** on the current timeframe (without triggering yet).
7. **Fetch HTF Values** in one `request.security` call (SMAs, DI/ADX).
8. **Combine Current & HTF Trend Filters** to confirm `Uptrend_Confirm` or `Downtrend_Confirm`.
9. **Check Reversal Conditions** (price crossing EMA or SMA short, in overbought/oversold zones).
10. **Enforce “One Trade Per Trend”** (clear `LastTradeTrend` whenever `CurrentTrend` flips).
11. **Enforce Cooldown** (must wait at least `Signal_Cooldown` bars since the prior signal).
12. **On Bar Close:**
* If `Raw_Long` AND not already in a long trend AND cooldown met, then fire `Long_Signal`.
* Else if `Raw_Short` AND not already in a short trend AND cooldown met, then fire `Short_Signal`.
* Otherwise, no new signal on this bar.
13. **Plot Long/Short Entry Shapes** according to whether it was a Trend signal or a Reversal signal.
14. **Send Alert** (“action=BUY” or “action=SELL”) exactly once per confirmed bar.
15. **If New Long/Short Signal, Set `InLong`/`InShort`, Record Entry Price, Initialize `Long_Max`/`Short_Min`.**
16. **While `InLong` is true:** Update `Long_Max = max(previous Long_Max, current high)`. Compute `SL_Long`. If the current trend flips (no longer uptrend), set `ExitLong = true`, plot a “TP X,” and close the position logic.
17. **While `InShort` is true:** Similarly update `Short_Min`, compute `SL_Short`, and if trend flips, set `ExitShort = true`, plot a “TP X,” and close the position logic.
18. **Optionally Display Debug Lines** (ATR SMA, ATR threshold, DI/ADX, HTF DI/ADX, etc.).
---
## 12. How to Use in TradingView Community
When you publish this indicator to the TradingView community—choosing “Protected” or “Invite-only” visibility—you can paste the above description into the “Description” field. Users will see exactly what each input does, how signals are generated, and what the various plotted lines represent, **without ever seeing the script source**. In this way, the code itself remains hidden but the logic is fully documented.
1. **Go to “Create New Indicator”** on TradingView.
2. **Paste Your Pine Code** (the full indicator script) in the Pine editor and save it.
3. **Set Visibility = Protected** (or Invite-only).
4. **In the “Description” Text Box, paste the entirety of this document** (steps 1–11).
5. **Click “Publish Script.”**
Users who view your indicator will see its name (“AI Strat Adaptive v3 (NoRepaint)”), a list of all inputs (with default values), and the detailed English description above. They can then load it on any chart, adjust inputs, and see the plotted signals, TP/SL lines, and optional debug overlays—without accessing the underlying Pine code.
---
### Summary of Key Points
* **RSI, EMA, ATR, DI/ADX, and “AI Score”** work together to define “trend vs. reversal.”
* **Dynamic volatility filter** uses ATR and ATR\_SMA to adapt the weighting of RSI vs. EMA and decide whether “volatility is high enough” to permit a trend trade.
* **One trade per detected trend** and a **cooldown period** prevent over‐trading.
* **Higher timeframe confirmation** (optional) further filters out noise.
* **No-repaint logic**:
* All signals only appear at bar close (`barstate.isconfirmed`).
* HTF values are fetched with `lookahead=barmerge.lookahead_off`.
* **Entry shapes** (triangles and circles) clearly mark trend vs. reversal entries.
* **Dynamic TP/SL**: highest‐high (or lowest‐low) since entry is used as TP, ATR×multiplier as SL.
* **Debug mode** (optional) shows every intermediate line for full transparency.
Use this description verbatim (or adapt it slightly for your personal style) when publishing. That way, your community sees exactly how each component works—inputs, functions, filters—while the Pine source code remains private.
AI SuperTrend x Pivot Percentile - Strategy [PresentTrading]█ Introduction and How it is Different
The AI SuperTrend x Pivot Percentile strategy is a sophisticated trading approach that integrates AI-driven analysis with traditional technical indicators. Combining the AI SuperTrend with the Pivot Percentile strategy highlights several key advantages:
1. Enhanced Accuracy in Trend Prediction: The AI SuperTrend utilizes K-Nearest Neighbors (KNN) algorithm for trend prediction, improving accuracy by considering historical data patterns. This is complemented by the Pivot Percentile analysis which provides additional context on trend strength.
2. Comprehensive Market Analysis: The integration offers a multi-faceted approach to market analysis, combining AI insights with traditional technical indicators. This dual approach captures a broader range of market dynamics.
BTC 6H L/S Performance
Local
█ Strategy: How it Works - Detailed Explanation
🔶 AI-Enhanced SuperTrend Indicators
1. SuperTrend Calculation:
- The SuperTrend indicator is calculated using a moving average and the Average True Range (ATR). The basic formula is:
- Upper Band = Moving Average + (Multiplier × ATR)
- Lower Band = Moving Average - (Multiplier × ATR)
- The moving average type (SMA, EMA, WMA, RMA, VWMA) and the length of the moving average and ATR are adjustable parameters.
- The direction of the trend is determined based on the position of the closing price in relation to these bands.
2. AI Integration with K-Nearest Neighbors (KNN):
- The KNN algorithm is applied to predict trend direction. It uses historical price data and SuperTrend values to classify the current trend as bullish or bearish.
- The algorithm calculates the 'distance' between the current data point and historical points. The 'k' nearest data points (neighbors) are identified based on this distance.
- A weighted average of these neighbors' trends (bullish or bearish) is calculated to predict the current trend.
For more please check: Multi-TF AI SuperTrend with ADX - Strategy
🔶 Pivot Percentile Analysis
1. Percentile Calculation:
- This involves calculating the percentile ranks for high and low prices over a set of predefined lengths.
- The percentile function is typically defined as:
- Percentile = Value at (P/100) × (N + 1)th position
- Where P is the desired percentile, and N is the number of data points.
2. Trend Strength Evaluation:
- The calculated percentiles for highs and lows are used to determine the strength of bullish and bearish trends.
- For instance, a high percentile rank in the high prices may indicate a strong bullish trend, and vice versa for bearish trends.
For more please check: Pivot Percentile Trend - Strategy
🔶 Strategy Integration
1. Combining SuperTrend and Pivot Percentile:
- The strategy synthesizes the insights from both AI-enhanced SuperTrend and Pivot Percentile analysis.
- It compares the trend direction indicated by the SuperTrend with the strength of the trend as suggested by the Pivot Percentile analysis.
2. Signal Generation:
- A trading signal is generated when both the AI-enhanced SuperTrend and the Pivot Percentile analysis agree on the trend direction.
- For instance, a bullish signal is generated when both the SuperTrend is bullish, and the Pivot Percentile analysis shows strength in bullish trends.
🔶 Risk Management and Filters
- ADX and DMI Filter: The strategy uses the Average Directional Index (ADX) and the Directional Movement Index (DMI) as filters to assess the trend's strength and direction.
- Dynamic Trailing Stop Loss: Based on the SuperTrend indicator, the strategy dynamically adjusts stop-loss levels to manage risk effectively.
This strategy stands out for its ability to combine real-time AI analysis with established technical indicators, offering traders a nuanced and responsive tool for navigating complex market conditions. The equations and algorithms involved are pivotal in accurately identifying market trends and potential trade opportunities.
█ Usage
To effectively use this strategy, traders should:
1. Understand the AI and Pivot Percentile Indicators: A clear grasp of how these indicators work will enable traders to make informed decisions.
2. Interpret the Signals Accurately: The strategy provides bullish, bearish, and neutral signals. Traders should align these signals with their market analysis and trading goals.
3. Monitor Market Conditions: Given that this strategy is sensitive to market dynamics, continuous monitoring is crucial for timely decision-making.
4. Adjust Settings as Needed: Traders should feel free to tweak the input parameters to suit their trading preferences and to respond to changing market conditions.
█Default Settings and Their Impact on Performance
1. Trading Direction (Default: "Both")
Effect: Determines whether the strategy will take long positions, short positions, or both. Adjusting this setting can align the strategy with the trader's market outlook or risk preference.
2. AI Settings (Neighbors: 3, Data Points: 24)
Neighbors: The number of nearest neighbors in the KNN algorithm. A higher number might smooth out noise but could miss subtle, recent changes. A lower number makes the model more sensitive to recent data but may increase noise.
Data Points: Defines the amount of historical data considered. More data points provide a broader context but may dilute recent trends' impact.
3. SuperTrend Settings (Length: 10, Factor: 3.0, MA Source: "WMA")
Length: Affects the sensitivity of the SuperTrend indicator. A longer length results in a smoother, less sensitive indicator, ideal for long-term trends.
Factor: Determines the bandwidth of the SuperTrend. A higher factor creates wider bands, capturing larger price movements but potentially missing short-term signals.
MA Source: The type of moving average used (e.g., WMA - Weighted Moving Average). Different MA types can affect the trend indicator's responsiveness and smoothness.
4. AI Trend Prediction Settings (Price Trend: 10, Prediction Trend: 80)
Price Trend and Prediction Trend Lengths: These settings define the lengths of weighted moving averages for price and SuperTrend, impacting the responsiveness and smoothness of the AI's trend predictions.
5. Pivot Percentile Settings (Length: 10)
Length: Influences the calculation of pivot percentiles. A shorter length makes the percentile more responsive to recent price changes, while a longer length offers a broader view of price trends.
6. ADX and DMI Settings (ADX Length: 14, Time Frame: 'D')
ADX Length: Defines the period for the Average Directional Index calculation. A longer period results in a smoother ADX line.
Time Frame: Sets the time frame for the ADX and DMI calculations, affecting the sensitivity to market changes.
7. Commission, Slippage, and Initial Capital
These settings relate to transaction costs and initial investment, directly impacting net profitability and strategy feasibility.
AI-JX Strategy### 🤖 Core Features
AI-JX v3.3 is an AI-powered comprehensive trading strategy system developed with PineScript v6, integrating multiple advanced technical analysis tools and machine learning algorithms.
### 📊 Main Functional Modules 1. AI Learning System
- Adaptive Parameter Optimization : Automatically learns and adjusts trading parameters
- Three Strategy Modes : Conservative (ranging markets), Aggressive (trending markets), Balanced (universal)
- Dynamic Weight Adjustment : Intelligently allocates weights to different strategies based on market conditions
- Learning Memory Mechanism : Records historical trading data for continuous strategy optimization 2. Technical Indicator System
- SuperTrend Indicator : ATR-based trend following system
- Heikin Ashi Smoothing : Reduces market noise for clearer trend signals
- Standard Deviation Channels : Multi-level support and resistance analysis
- Trend Distribution Profile : Visualizes price distribution and trend strength
- Multi-Timeframe Analysis : Comprehensive analysis across 5m, 15m, and 1h timeframes 3. Intelligent Signal Generation
- Traditional Signals : Classic buy/sell signals based on SuperTrend
- AI Smart Signals : Comprehensive scoring system combining RSI, MACD, and ATR
- False Breakout Detection : Identifies and filters fake breakout signals
- Price Confirmation Mechanism : Ensures signal validity and reliability 4. Risk Management System
- Dynamic Stop Loss/Take Profit : Long 3% TP/1.5% SL, Short 2:1 risk-reward ratio
- Slippage Monitoring : Real-time market slippage risk assessment
- Volatility Filtering : Adjusts trading strategy based on ATR
- Position Management : Smart capital allocation and risk control 5. Visualization Panels
- Statistics Panel : Displays key data like trade count, win rate, current strategy
- AI Learning Panel : Shows strategy weights and learning progress
- Prediction Panel : Real-time AI analysis and trading recommendations
- Chart Markers : Clear buy/sell signals and trend line displays 6. Alert System
- Multiple Alert Types : Buy, sell, take profit, and stop loss notifications
- Personalized Messages : Fun "WangWang" themed alert messages
- Real-time Notifications : Precise alerts with maximum one per bar frequency
### 🎯 Key Advantages
- AI-Driven : Machine learning optimization for better performance
- Multi-Strategy : Adapts to different market conditions automatically
- Risk-Controlled : Comprehensive risk management with dynamic adjustments
- User-Friendly : Intuitive interface with detailed visualization panels
- Highly Customizable : Extensive parameter settings for different trading styles
AI x Meme Impulse Tracker [QuantraSystems]AI x Meme Impulse Tracker
Quantra Systems guarantees that the information created and published within this document and on the Tradingview platform is fully compliant with applicable regulations, does not constitute investment advice, and is not exclusively intended for qualified investors.
Important Note!
The system equity curve presented here has been generated as part of the process of testing and verifying the methodology behind this script.
Crucially, it was developed after the system was conceptualized, designed, and created, which helps to mitigate the risk of overfitting to historical data. In other words, the system was built for robustness, not for simply optimizing past performance.
This ensures that the system is less likely to degrade in performance over time, compared to hyper-optimized systems that are tailored to past data. No tweaks or optimizations were made to this system post-backtest.
Even More Important Note!!
The nature of markets is that they change quickly and unpredictably. Past performance does not guarantee future results - this is a fundamental rule in trading and investing.
While this system is designed with broad, flexible conditions to adapt quickly to a range of market environments, it is essential to understand that no assumptions should be made about future returns based on historical data. Markets are inherently uncertain, and this system - like all trading systems - cannot predict future outcomes.
Introduction
The AI x Meme Impulse Tracker is a cutting-edge, fast-acting rotational algorithm designed to capitalize on the strength of assets within pre-selected categories. Using a custom function built on top of the RSI Pulsar, the system measures momentum through impulses rather than traditional trend following methods. This allows for swifter reallocations based on short bursts of strength.
This system focuses on precision and agility - making it highly adaptable in volatile markets. The strategy is built around three independent asset categories - with allocations only made to the strongest asset in each - ensuring that capital movement (in particular between blockchains) is kept to a minimum for efficiency purposes while maintaining exposure to the highest performing tokens.
Legend
Token Inputs:
The Impulse Tracker is designed with dynamic asset selection - allowing traders to customize the inputs for each category. This feature enables flexible system management, as the number of active tokens within each category can be adjusted at any time. Whether the user chooses the default of 13 tokens per category, or fewer, the system will automatically recalibrate. This ensures that all calculations, from relative strength to individual performance assessments, adjust as required. Disabled tokens are treated by the system as if they don’t exist - seamlessly updating performance metrics and the Impulse Tracker’s allocation behavior to maintain the highest level of efficiency and accuracy.
System Equity Curve:
The Impulse Tracker plots both the rotational system’s equity and the Buy-and-Hold (or ‘HODL’) benchmark of Bitcoin for comparison. While the HODL approach allocates the entire portfolio to Bitcoin and functions as an index to compare to, the Impulse Tracker dynamically allocates based on strength impulses within the chosen tokens and categories. The system equity curve is representative of adding an equal capital split between the strongest assets of each category. The relative strength system does handle ‘ties’ of strength - in this situation multiple tokens from a single category can be included in the final equity curve, with the allocated weight to that category split between the tied assets.
TABLES:
Equity Stats:
This table is held in Quantra System's typical UI design language. It offers a comprehensive snapshot of the system’s performance, with key metrics organized to help traders quickly assess both short-term and cumulative results. The left side provides details on individual asset performance, while the right side presents a comparison of the system’s risk-adjusted metrics against a simple BTC Hodl strategy.
The leftmost column of the Equity Stats table showcases performance indicators for the system’s current allocations. This provides quick identification of the current strongest tokens, based on confirmed and non-repainting data as soon as the current opens and the last bar closes.
The right-hand side compares the performance differences between the system and Hodl profits, both on a cumulative basis and analyzing only the previous bar. The total number of position changes is also tracked in this table - an important metric when calculating total slippage and should be used to determine how ‘hands-on’ the strategy will be on the current timeframe.
The lower part of the table highlights a direct comparison of the AI x Memes Impulse strategy with buy-and-hold Bitcoin. The risk adjusted performance ratios, Sharpe, Sortino and Omega, are shown side by side, as well as the maximum drawdown experienced by both strategies within the set testing window.
Screener Table:
This table provides a detailed breakdown of the performance for each asset that has been the strongest in its category at some point and thus received an allocation. The table tracks several key metrics for each asset - including returns, volatility, Sharpe ratio, Sortino ratio, Omega ratio, and maximum drawdown. It also displays the signals for both current and previous periods, as well as the assets weight in the theoretical portfolio. Assets that have never received a signal are also included, giving traders an overview of which assets have contributed to the portfolio's performance and which have not played a role so far.
The position changes cell also offers important insights, as it shows the frequency of not just total position changes, but also rebalancing events.
Detailed Slippage Table:
The Detailed Slippage Table provides a comprehensive breakdown of the calculated slippage and fees incurred throughout the strategy’s operations. It contains several key metrics that give traders a granular view of the costs associated with executing the system:
Selected Slippage - Displays the current slippage rate, as defined in the input menu.
Removal Slippage - This accounts for any slippage or fees incurred when removing an allocation from a token.
Reallocation Slippage - Tracks the slippage or fees when reallocating capital to existing positions.
Addition Slippage - Measures the slippage or fees incurred when allocating capital to new tokens.
Final Slippage - Is the sum of all the individual slippage points and provides a quick view of the total slippage accounted for by the system.
The table is also divided into two columns:
Last Transaction Slippage + Fees - Displays any slippage or fees incurred based on position changes within the current bar.
Total Slippage + Fees - Shows the cumulative slippage and fees incurred since the portfolio’s selected start date.
Visual Customization:
Several customizable features are included within the input menu to enhance user experience. These include custom color palettes, both preloaded and user-selectable. This allows traders to personalize the visual appearance of the tables, ensuring clarity and consistency with their preferred interface themes and background coloring.
Additionally, users can adjust both the position and sizes of all the tables - enabling complete tailoring to the trader’s layout and specific viewing preferences and screen configurations. This level of customization ensures a more intuitive and flexible interaction with the system’s data.
Core Features and Methodologies
Advanced Risk Management - A Unique Filtering Approach:
The Equity Curve Activation Filter introduces an innovative way to dynamically manage capital allocation, aligning with periods of market trend strength. This filter is rooted in the understanding that markets move cyclically - altering between periods trending and mean-reverting periods. This cycle is especially pronounced in the crypto markets, where strong uptrends are often followed by prolonged periods of sideways movements or corrections as participants take profits and momentum fades.
The Cyclical Nature of Markets and Trend Following:
Financial markets do not trend indefinitely. Each uptrend or downtrend, whether over high and low timeframes, tends to culminate in a phase where momentum exhausts - leading to the sideways or corrective phases. This cycle results from the natural dynamics of market participants: during extended trends, more participants jump in, riding the momentum until profit taking causes the trend to slow down or reverse. This cyclical behavior occurs across all timeframes and in all markets - making it essential to adapt trading strategies in attempt to minimize losses during less favorable conditions.
In a trend following system, profitability often mirrors this cyclical pattern. Trend following strategies thrive when markets are moving directionally, capturing gains as price moves with strength in a single direction. However in phases where the market chops sideways, trend following strategies will usually experience drawdowns and reduced returns due to the impersistent nature of any trends. This fluctuation in trend following profitability can actually serve as one of the best coincident indicators of broader market regime change - when profitability begins to fade, it often signals a transition to drawn out unfavorable trend trading conditions.
The Equity Curve as a Market Signal
Within the Impulse Tracker, a continuous equity curve is calculated based upon the system's allocation to the strongest tokens. This equity curve effectively tracks the system’s performance under all market conditions. However, instead of solely relying on the direct performance of the selected tokens, the system applies additional filters to analyze the trend strength of this equity curve itself.
In the same way you only want to purchase an asset that is moving up in price, you only want to allocate capital to a strategy whose equity curve is trending upwards!
The Equity Curve Activation Filter consistently monitors the trend of this equity curve through various filter indicators, such as the “Wave Pendulum Trend”, the “Quasar QSM” and the “MAQSM” (an aggregate of multiple types of averages). These filters help determine whether the equity curve is trending upwards, signaling a favorable period for trend following. When the equity curve is in a positive trend, capital is allocated to the system as normal - allowing it to capture gains during favorable market conditions, Conversely, when the trend weakens and the equity curves begins to stagnate or decline, the activation filter shifts the system into a “cash” positions - temporarily halting allocations in order to prevent market exposure during choppy or mean reverting phases.
Timing Allocation With Market Conditions
This unique filtering approach ensures that the system is primarily active during periods when market trends are most supportive. By aligning capital allocations with the uptrend in trend following profitability, the system is designed to enter during periods of strong momentum and move to cash when momentum with the equity curve wanes. This approach reduces the risk of overtrading in less favorable conditions and preserves capital for the next favorable trend.
In essence the Equity Curve Allocation Filter serves as a dynamic risk management layer that leverages the cyclicality of trend following profitability in order to navigate shifting market phases.
Sensitivity and Signal Responsiveness:
The Quasar Sensitivity Setting allows users to fine-tune the system’s responsiveness to asset signals. High sensitivity settings lead to quicker position changes, making the system highly reactive to short term strength impulses. This is especially useful in fast moving markets where token strength can shift rapidly. The Sensitive setting might be more applicable to higher volatility or lower market cap assets - as the increased volatility increases the necessity of faster position cutting in order to front run the crowd. Of course - a balanced approach is ideal, as if the signals are too fast there will be too many whips and false signals. (And extra fees + slippage!)
The benefit of this script is because of the advanced slippage calculations, false signals are sufficiently punished (unlike systems without fees or slippage) - so it will become immediately apparent if the false signals have a significantly detrimental impact on the system’s equity curve.
Asset specific signals within each category are re-evaluated after the close of each bar to ensure that capital is always allocated to the highest performing asset. If a token’s momentum begins to fade the system swiftly reallocates to the next strongest asset within that category.
Category Filter - Allocates only to the Strongest Asset per group
One of the core innovations of the AI x Meme Impulse Tracker is the customizable Category Filter, which ensures that only the strongest-performing asset within each predefined group receives capital allocation. This approach not only increases the precision of asset selection but also allows traders to tailor the system to specific token narratives or categories. Sectors can include trending themes such as high-attention meme tokens, AI-driven tokens, or even categorize assets by blockchain ecosystems like Ethereum, Solana, or Base chain. This flexibility enables users to align their strategies with the latest market narratives or to optimize for specific groups, focusing on high-beta tokens within well defined sectors for a more targeted exposure. By keeping the focus on category leaders, the system avoids diluting its impact across underperforming assets, thereby maximizing capital efficiency and reducing unnecessary trading costs.
Dynamic Asset Reallocation:
Dynamic reallocation ensures that the system remains nimble and adapts to changing market conditions. Unlike slower systems, the Quasar method continually monitors for changes in asset strength and reallocates capital accordingly - ensuring that the system is always positioned in the highest performing assets within each category.
Position Changes and Slippage:
The Impulse Tracker places a strong emphasis on realistic simulation, prioritizing accuracy over inflated backtest results. This approach ensures that slippage is accounted for in a more aggressive manner than what may be experienced in real-world execution.
Each position change within the system - whether it’s buying, selling, reallocating, or rebalancing between assets - incurs slippage. Slippage is applied to both ends of every transaction: when a position is entered and exited, and when reallocating capital from one token to another. This dynamic behavior is further enhanced by a customizable slippage/fees input, allowing users to simulate realistic transaction costs based on their own market conditions and execution behaviors.
The slippage model works by applying a weighted slippage to the equity curve, taking into account the actual amount of capital being moved. Slippage is not applied in a blanket manner but rather in proportion to the allocation changes. For example, if the system reallocates from a single 100% position to two 50% allocations, slippage will be applied to the 50% removed from the first asset and the 50% added to the new asset, resulting in a 1x slippage multiplier.
This process becomes more granular when multiple assets are involved. For instance, if reallocating from two 50% positions to three 33% positions, slippage will be incurred on each of the changes, but at a reduced rate (⅔ x slippage), reflecting the smaller percentage of portfolio equity being moved. The slippage model accounts for all types of allocation shifts, whether increasing or decreasing the number of tokens held, providing a realistic assessment of system costs.
Here are some detailed examples to illustrate how slippage is calculated based on different scenarios:
100% → 50% / 50%: 1x slippage applied to both position changes (2 allocation changes).
50% / 50% → 33% / 33% / 33%: ⅔ x slippage multiplier applied across 3 allocation changes.
33% / 33% / 33% → 100%: 4/3 x slippage multiplier applied across 3 allocation changes.
In practice, not every position change will be rebalanced perfectly, leading to a lower number of transactions and lower costs in practice. Additionally, with the use of limit orders, a trader can easily reduce the costs of entering a position, as well as ensuring a competitive entry price.
By simulating slippage in this granular manner, the system captures the absolute maximum level of fees and slippage, in order to ensure that backtest results lean towards an underrepresentation - opposed to inflated results compared with practical execution.
A Special Note on Slippage
In the image above, the system has been applied to four different timeframes - 20h, 15h, 10h, and 5h - using identical settings and a selected slippage amount of 2%. By isolating a recent trend leg, we can illustrate an important concept: while the 15h timeframe is more profitable than the 20h timeframe, this difference stems from a core trading principle. Lower timeframes typically provide more data points and allow for quicker entries and exits in a robust system. This often results in reduced downside and compounding of gains.
However, slippage, fees, and execution constraints are limiting factors, especially in volatile, low-cap cryptocurrencies. Although lower timeframes can improve performance by increasing trade frequency, each trade incurs heavy slippage costs that accumulate - impacting the portfolio’s capital at a compounding rate. In this example, the chosen slippage rate of 2% per trade is designed to reflect the realistic trading costs, emphasizing how lower timeframe trading comes at the cost of increased slippage and fees
Finding the optimal balance between timeframe and slippage impact requires careful consideration of factors such as portfolio size, liquidity of selected tokens, execution speed, and the fee rate of the exchange you execute trades on.
Equity Curve and Performance Calculations
To provide a benchmark, the script also generates a Buy-and-Hold (or "HODL") equity curve that represents a complete allocation to Bitcoin. This allows users to easily compare the performance of the dynamic rotation system with that more traditional benchmark strategy.
The script tracks key performance metrics for both the dynamic portfolio and the HODL strategy, including:
Sharpe Ratio
The Sharpe Ratio is a key metric that evaluates a portfolio’s risk-adjusted return by comparing its ‘excess’ return to its volatility. Traditionally, the Sharpe Ratio measures returns relative to a risk-free rate. However, in our system’s calculation, we omit the risk-free rate and instead measure returns above a benchmark of 0%. This adjustment provides a more universal comparison, especially in the context of highly volatile assets like cryptocurrencies, where a traditional risk-free benchmark, such as the usual 3-month T-bills, is often irrelevant or too distant from the realities of the crypto market.
By using 0% as the baseline, we focus purely on the strategy's ability to generate raw returns in the face of market risk, which makes it easier to compare performance across different strategies or asset classes. In an environment like cryptocurrency, where volatility can be extreme, the importance of relative return against a highly volatile backdrop outweighs comparisons to a risk-free rate that bears little resemblance to the risk profile of digital assets.
Sortino Ratio
The Sortino Ratio improves upon the Sharpe Ratio by specifically targeting downside risk and leaves the upside potential untouched. In contrast to the Sharpe Ratio (which penalizes both upside and downside volatility), the Sortino Ratio focuses only on negative return deviations. This makes it a more suitable metric for evaluating strategies like the AI x Meme Impulse Tracker - that aim to minimize drawdowns without restricting upside capture. By measuring returns relative to a 0% baseline, the Sortino ratio provides a clearer assessment of how well the system generates gains while avoiding substantial losses in highly volatile markets like crypto.
Omega Ratio
The Omega Ratio is calculated as the ratio of gains to losses across all return thresholds, providing a more complete view of how the system balances upside and downside risk even compared to the Sortino Ratio. While it achieves a similar outcome to the Sortino Ratio by emphasizing the system's ability to capture gains while limiting losses, it is technically a mathematically superior method. However, we include both the Omega and Sortino ratios in our metric table, as the Sortino Ratio remains more widely recognized and commonly understood by traders and investors of all levels.
Usage Summary:
While the backtests in this description are generated as if a trader held a portfolio of just the strongest tokens, this was mainly designed as a method of logical verification and not a recommended investment strategy. In practice, this system can be used in multiple ways.
It can be used as above, or as a factor in forming part of a broader asset selection system, or even a method of filtering tokens by strength in order to inform a day trader which tokens might be optimal to look for long-only trading setups on an intrabar timeframe.
Final Summary:
The AI x Meme Impulse Tracker is a powerful algorithm that leverages a unique strength and impulse based approach to asset allocation within high beta token categories. Built with a robust risk management framework, the system’s Equity Curve Activation Filter dynamically manages capital exposure based on the cyclical nature of market trends, minimizing exposure during weaker phases.
With highly customizable settings, the Impulse Tracker enables precise capital allocation to only the strongest assets, informed by real-time metrics and rigorous slippage modeling in order to provide the best view of historical profitability. This adaptable design, coupled with advanced performance analytics, makes it a versatile tool for traders seeking an edge in fast moving and volatile crypto markets.
AI Moving Average (Expo)█ Overview
The AI Moving Average indicator is a trading tool that uses an AI-based K-nearest neighbors (KNN) algorithm to analyze and interpret patterns in price data. It combines the logic of a traditional moving average with artificial intelligence, creating an adaptive and robust indicator that can identify strong trends and key market levels.
█ How It Works
The algorithm collects data points and applies a KNN-weighted approach to classify price movement as either bullish or bearish. For each data point, the algorithm checks if the price is above or below the calculated moving average. If the price is above the moving average, it's labeled as bullish (1), and if it's below, it's labeled as bearish (0). The K-Nearest Neighbors (KNN) is an instance-based learning algorithm used in classification and regression tasks. It works on a principle of voting, where a new data point is classified based on the majority label of its 'k' nearest neighbors.
The algorithm's use of a KNN-weighted approach adds a layer of intelligence to the traditional moving average analysis. By considering not just the price relative to a moving average but also taking into account the relationships and similarities between different data points, it offers a nuanced and robust classification of price movements.
This combination of data collection, labeling, and KNN-weighted classification turns the AI Moving Average (Expo) Indicator into a dynamic tool that can adapt to changing market conditions, making it suitable for various trading strategies and market environments.
█ How to Use
Dynamic Trend Recognition
The color-coded moving average line helps traders quickly identify market trends. Green represents bullish, red for bearish, and blue for neutrality.
Trend Strength
By adjusting certain settings within the AI Moving Average (Expo) Indicator, such as using a higher 'k' value and increasing the number of data points, traders can gain real-time insights into strong trends. A higher 'k' value makes the prediction model more resilient to noise, emphasizing pronounced trends, while more data points provide a comprehensive view of the market direction. Together, these adjustments enable the indicator to display only robust trends on the chart, allowing traders to focus exclusively on significant market movements and strong trends.
Key SR Levels
Traders can utilize the indicator to identify key support and resistance levels that are derived from the prevailing trend movement. The derived support and resistance levels are not just based on historical data but are dynamically adjusted with the current trend, making them highly responsive to market changes.
█ Settings
k (Neighbors): Number of neighbors in the KNN algorithm. Increasing 'k' makes predictions more resilient to noise but may decrease sensitivity to local variations.
n (DataPoints): Number of data points considered in AI analysis. This affects how the AI interprets patterns in the price data.
maType (Select MA): Type of moving average applied. Options allow for different smoothing techniques to emphasize or dampen aspects of price movement.
length: Length of the moving average. A greater length creates a smoother curve but might lag recent price changes.
dataToClassify: Source data for classifying price as bullish or bearish. It can be adjusted to consider different aspects of price information
dataForMovingAverage: Source data for calculating the moving average. Different selections may emphasize different aspects of price movement.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
AI-Powered ScalpMaster Pro [By TraderMan]🧠 AI-Powered ScalpMaster Pro How It Works
📊 What Is the Indicator and What Does It Do?
🧠 AI-Powered ScalpMaster Pro is a powerful technical analysis tool designed for scalping (short-term, fast-paced trading) in financial markets such as forex, crypto, or stocks. It combines multiple technical indicators (RSI, MACD, Stochastic, Momentum, EMA, SuperTrend, CCI, and OBV) to identify market trends and generate AI-driven buy (🟢) or sell (🔴) signals. The goal is to help traders seize profitable scalping opportunities with quick and precise decisions. 🚀
Key Features:
🧠 AI-Driven Logic: Analyzes signals from multiple indicators to produce reliable trend signals.
📈 Signal Strength: Displays buy (bull) and sell (bear) signal strength as percentages.
✅ Success Rate: Tracks the performance of the last 5 trades and calculates the success rate.
🎯 Entry, TP, and SL Levels: Automatically sets entry points, take profit (TP), and stop loss (SL) levels.
📏 EMA Zone: Analyzes price movement around the EMA 200 to confirm trend direction.
⚙️ How Does It Work?
The indicator uses a scoring system by combining the following technical indicators:
RSI (14): Evaluates whether the price is in overbought or oversold zones.
MACD (12, 26, 9): Analyzes trend direction and momentum.
Stochastic (%K): Measures the speed of price movement.
Momentum: Checks the price change over the last 10 bars.
EMA 200: Determines the long-term trend direction.
SuperTrend: Tracks trends based on volatility.
CCI (20): Measures price deviation from its normal range.
OBV ROC: Analyzes volume changes.
Each indicator generates a buy (bull) or sell (bear) signal. If 6 or more indicators align in the same direction (e.g., bullScore >= 6 for buy), the indicator produces a strong trend signal:
📈 Strong Buy Signal: bullScore >= 6 and bullScore > bearScore.
📉 Strong Sell Signal: bearScore >= 6 and bearScore > bullScore.
🔸 Neutral: No dominant direction.
Additionally, the EMA Zone feature confirms the trend based on the price’s position relative to a zone around the EMA 200:
Price above the zone and sufficiently distant → Uptrend (UP). 🟢
Price below the zone and sufficiently distant → Downtrend (DOWN). 🔴
Price within the zone → Neutral. 🔸
🖥️ Display on the Chart
Table: A table in the top-right corner shows the status of all indicators (✅ Buy / ❌ Sell), signal strength (as %), success rate, and results of the last 5 trades.
Lines and Labels:
🎯 Entry Level: A gray line at the price level when a new signal is generated.
🟢 TP (Take Profit): A green line showing the take-profit level.
🔴 SL (Stop Loss): A red line showing the stop-loss level.
EMA Zone: The EMA 200 and its surrounding colored zone visualize the trend direction (green: uptrend, red: downtrend, gray: neutral).
📝 How to Use It?
Platform Setup:
Add the indicator to the TradingView platform.
Customize settings as needed (e.g., EMA length, risk/reward ratio).
Monitoring Signals:
Check the table: Look for 📈 STRONG BUY or 📉 STRONG SELL signals to prepare for a trade.
AI Text: Trust signals more when it says "🧠 FULL CONFIDENCE" (success rate ≥ 50%). Be cautious if it says "⚠️ LOW CONFIDENCE."
Entering a Position:
🟢 Buy Signal:
Table shows "📈 STRONG BUY" and bullScore >= 6.
Price is above the EMA Zone (green zone).
Entry: Current price (🎯 entry line).
TP: 2% above the entry price (🟢 TP line).
SL: 1% below the entry price (🔴 SL line).
🔴 Sell Signal:
Table shows "📉 STRONG SELL" and bearScore >= 6.
Price is below the EMA Zone (red zone).
Entry: Current price (🎯 entry line).
TP: 2% below the entry price (🟢 TP line).
SL: 1% above the entry price (🔴 SL line).
Position Management:
If the price hits TP, the trade closes profitably (✅ Successful).
If the price hits SL, the trade closes with a loss (❌ Failed).
Results are updated in the "Last 5 Trades" section of the table.
Risk Management:
Default risk/reward ratio is 1:2 (1% risk, 2% reward).
Always adjust position size based on your capital.
Consider smaller lot sizes for "⚠️ LOW CONFIDENCE" signals.
💡 Tips
Timeframe: Use 1-minute, 5-minute, or 15-minute charts for scalping.
Market Selection: Works best in volatile markets (e.g., BTC/USD, EUR/USD).
Confirmation: Ensure the EMA Zone trend aligns with the signal.
Discipline: Stick to TP and SL levels, avoid emotional decisions.
⚠️ Warnings
No indicator is 100% accurate. Always use additional analysis (e.g., support/resistance).
Be cautious during high-volatility periods (e.g., news events).
The success rate is based on past performance and does not guarantee future results.
Ai BTC Signals Buy & Whales / Liquidation - Strategy [Ai Whales]Dear Trader,
The development involved professional analysts and incorporated AI to adapt signals to the modern, constantly changing, and highly volatile BTCUSD market, also taking into account the presence and actions of large institutional players — the so-called "Whales." The strategy allows you to instantly evaluate any configuration you set within the indicator and see the results reflected in professional performance metrics aligned with your chosen strategy.
The indicator displays several signals on the chart:
1) Buy signal (not sell signals)
2) Take profit line and price
3) Stop loss line and price
4) Manipulations & Liquidations observed in the market
5) Whale activity—buying in small, medium, and large amounts
The indicator does not repaint because it is based on showing signals only after the candle closes, so the calculations are true and not distorted.
Recommended pair: BTCUSD ; BTCUSDT ; BTCUSDTP and same.
The indicator can show R/R - 0.5:1 1:1 1:2 1:3 1:4
Recommended timeframes for use: from 4 hours up to 1 week, with the ideal being 1 day. However, you are free to experiment with other near timeframes.
Possible trading modes: spot or futures.
Some methods used in the calculations of the indicator:
- statistical patterns that have the ability to repeat in the future. Bitcoin cycles in different market phases that also have the ability to repeat and are included in the indicator,
- miners' capitulation and hashrate level are also taken into account by the indicator,
- candle volumes and their deltas are taken into account in the calculations,
- as well as other bases such as RSI and its divergence, the crossing of EMA of various configurations and etc.
**How the strategy calculates positions:**
A position opens at the Buy signal level and is fixed at the level of the thick green line, which serves as the primary take profit target. Pyramiding (adding to positions) can be enabled in the settings.
The size of each position is adjustable via the settings. Importantly, each signal creates its own take profit lines. When pyramiding is enabled, all positions are eventually closed at the nearest take profit level generated by any of the pyramiding signals. This approach minimizes potential losses if the price doesn’t reach the maximum take profit levels initially set; the strategy closes positions at the closest available take profit level. This conservative method for strategy reduces risk, although ideally, each position in the pyramid should be closed at an individual take-profit level, which would lead to even better results during deep backtesting.
The strategy includes alerts that can be configured based on your platform’s capabilities. Alerts trigger on the chart when "Buy" or "Whale" signals are detected.
**Settings Overview:**
- Inside the strategy: default platform options.
- Inside the indicato have some filters:
1) allows traders to choose display modes
2) position entries based on market phase—rising or falling
3) can also select whether to trade after manipulations and liquidations
4) can also select whether to trade after whale activity (small medium or big amounts of whales).
You can manually adjust take profit and stop loss levels via simple method selections, making these flexible yet user-friendly. The indicator offers three main styles:
- "Universal" (standard levels)
- "Aggressive"
- "Conservative"
**Performance and caveats:**
Deep Backtested from day one of Bitcoin’s listing on various exchanges under specific conditions (no liquidations, certain settings), the indicator has shown a maximum drawdown of about 5-15%, with final returns surpassing "buy and hold" more than 1000000% and WinRate 93-100% However, it’s crucial to understand that such spectacular past performance does not guarantee future results.
If you are serious about your investments, remember that geopolitical events, institutional shifts, or other unforeseen factors can significantly impact Bitcoin’s price—or even its existence. Unfortunately, AI has not yet learned to fully account for these macro conditions within its adaptive mechanisms.
Trade wisely, and use this powerful tool responsibly.
Best regards,
AI KNN-Dual SuperTrend MTF - by Trading Pine Lab🇬🇧
The AI KNN-Dual SuperTrend MTF is a next-generation trading strategy that merges two higher-timeframe SuperTrends with dual KNN (K-Nearest Neighbors) classifiers, an ADX/DMI filter, and a Pivot Percentile bias module. This layered architecture ensures stronger signal confirmation by requiring consensus across AI models, multi-timeframe SuperTrends, and statistical filters.
Entries occur only when both SuperTrends align with bullish or bearish KNN labels, while the ADX/DMI filter validates momentum. Exits are managed dynamically with adaptive trailing stops (ST ± ATR × factor) or when market conditions flip according to percentile bias.
All parameters are fully configurable:
-Trading direction filter: Long / Short / Both.
-KNN classifiers: neighbors (K), dataset size (N), smoothing lengths.
-Dual SuperTrend: higher timeframes, ATR length, ATR factor, baseline type.
-ADX/DMI filter: customizable length and timeframe.
-Pivot Percentile module: multi-scale statistical bias.
-Visualization: AI markers, ribbons, aura lines, and per-trend coloring.
AI Volume-KNN SuperTrend - by Trading Pine Lab🇬🇧 English
The AI Volume-KNN SuperTrend is an advanced trading strategy that combines the robustness of the SuperTrend indicator with a machine-learning inspired KNN (K-Nearest Neighbors) model. The baseline is built from a volume-weighted moving average with ATR-based bands, while the KNN classifier validates trend direction in real time. This dual-layer approach reduces false signals and improves trend confirmation.
Entries are triggered when the SuperTrend flips direction and the KNN classifier confirms the move as bullish or bearish. Exits are managed with a dynamic trailing stop, automatically adjusting to SuperTrend ± ATR × factor. The strategy includes visual markers for AI start/continuation signals, as well as customizable coloring for bullish, bearish, and neutral phases.
All parameters are fully configurable:
-Trading direction filter: Long / Short / Both.
-KNN settings: number of neighbors (K), dataset size (N).
-Label smoothing: price and SuperTrend smoothing lengths (WMAs).
-SuperTrend settings: length, ATR factor, and moving average source.
-Visualization: trend markers and per-trend coloring.
AI A++ Liquidity Sweep FVGThat is a critical question. For the "AI A++ Liqu-idity Sweep FVG" indicator to work exactly as designed, you must have your chart set to the:
1-Minute (1m) Timeframe
The Reason:
The logic of the script is built to analyze the very specific, rapid price action that occurs in the first few minutes of the New York session open.
FVG Detection: A Fair Value Gap is a three-candle pattern. On the 1-minute chart, this allows us to see the rapid imbalances created by the opening burst of volume. On a higher timeframe like the 5-minute or 15-minute, these subtle but powerful gaps would be smoothed over and might not even be visible.
Liquidity Sweep Precision: The script is looking for a quick "stop hunt" that pierces the pre-market high or low and then immediately reverses. This action is most clearly and accurately seen on the 1-minute chart.
Using any other timeframe will cause the indicator to analyze the market incorrectly and either miss valid setups or provide false signals.
So, to confirm your setup for Monday morning:
Instrument: MNQ (Micro E-mini Nasdaq-100 Futures)
Timeframe: 1-Minute
Indicator: "AI A++ Liquidity Sweep FVG" active on the chart.
Alert: Alert set up for the indicator.
You are now perfectly set up to catch the exact A++ setup we are waiting for.
AI Trend Momentum SniperThe AI Trend Momentum Sniper is a powerful technical analysis tool designed for day trading. This strategy combines multiple momentum and trend indicators to identify high-probability entry and exit points. The indicator utilizes a combination of Supertrend, MACD, RSI, ATR (Average True Range), and On-Balance Volume (OBV) to generate real-time signals for buy and sell opportunities.
Key Features:
Supertrend for detecting market direction (bullish or bearish).
MACD for momentum confirmation, highlighting changes in market momentum.
RSI to filter out overbought/oversold conditions and ensure high-quality trades.
ATR as a volatility filter to adjust for changing market conditions.
OBV (On-Balance Volume) to confirm volume strength and trend validity.
Dynamic Stop-Loss & Take-Profit based on ATR to manage risk and lock profits.
This indicator is tailored for intraday traders looking for quick market moves, especially in volatile and high liquidity assets like Bitcoin (BTC) and Ethereum (ETH). It helps traders capture short-term trends with efficient risk management tools.
How to Apply:
Set Your Chart: Apply the AI Trend Momentum Sniper to a 5-minute (M5) or 15-minute (M15) chart for optimal performance.
Buy Signal: When the indicator generates a green arrow below the bar, it indicates a buy signal based on positive trend and momentum alignment.
Sell Signal: A red arrow above the bar signals a sell condition when the trend and momentum shift bearish.
Stop-Loss and Take-Profit: The indicator automatically calculates dynamic stop-loss and take-profit levels based on the ATR value for each trade, ensuring proper risk management.
Alerts: Set up custom alerts for buy or sell signals, and get notified instantly when opportunities arise.
Best Markets for Use:
BTC/USDT, ETH/USDT – High liquidity and volatility.
Major altcoins with sufficient volume.
Avoid using it on low-liquidity assets where price action may become erratic.
Timeframes:
This indicator is best suited for lower timeframes (5-minute to 15-minute charts) to capture quick price movements in trending markets.
AI InfinityAI Infinity – Multidimensional Market Analysis
Overview
The AI Infinity indicator combines multiple analysis tools into a single solution. Alongside dynamic candle coloring based on MACD and Stochastic signals, it features Alligator lines, several RSI lines (including glow effects), and optionally enabled EMAs (20/50, 100, and 200). Every module is individually configurable, allowing traders to tailor the indicator to their personal style and strategy.
Important Note (Disclaimer)
This indicator is provided for educational and informational purposes only.
It does not constitute financial or investment advice and offers no guarantee of profit.
Each trader is responsible for their own trading decisions.
Past performance does not guarantee future results.
Please review the settings thoroughly and adjust them to your personal risk profile; consider supplementary analyses or professional guidance where appropriate.
Functionality & Components
1. Candle Coloring (MACD & Stochastic)
Objective: Provide an immediate visual snapshot of the market’s condition.
Details:
MACD Signal: Used to identify bullish and bearish momentum.
Stochastic: Detects overbought and oversold zones.
Color Modes: Offers both a simple (two-color) mode and a gradient mode.
2. Alligator Lines
Objective: Assist with trend analysis and determining the market’s current phase.
Details:
Dynamic SMMA Lines (Jaw, Teeth, Lips) that adjust based on volatility and market conditions.
Multiple Lengths: Each element uses a separate smoothing period (13, 8, 5).
Transparency: You can show or hide each line independently.
3. RSI Lines & Glow Effects
Objective: Display the RSI values directly on the price chart so critical levels (e.g., 20, 50, 80) remain visible at a glance.
Details:
RSI Scaling: The RSI is plotted in the chart window, eliminating the need to switch panels.
Dynamic Transparency: A pulse effect indicates when the RSI is near critical thresholds.
Glow Mode: Choose between “Direct Glow” or “Dynamic Transparency” (based on ATR distance).
Custom RSI Length: Freely adjustable (default is 14).
4. Optional EMAs (20/50, 100, 200)
Objective: Utilize moving averages for trend assessment and identifying potential support/resistance areas.
Details:
20/50 EMA: Select which one to display via a dropdown menu.
100 EMA & 200 EMA: Independently enabled.
Color Logic: Automatically green (price > EMA) or red (price < EMA). Each EMA’s up/down color is customizable.
Configuration Options
Candle Coloring:
Choose between Gradient or Simple mode.
Adjust the color scheme for bullish/bearish candles.
Transparency is dynamically based on candle body size and Stochastic state.
Alligator Lines:
Toggle each line (Jaw/Teeth/Lips) on or off.
Select individual colors for each line.
RSI Section:
RSI Length can be set as desired.
RSI lines (0, 20, 50, 80, 100) with user-defined colors and transparency (pulse effect).
Additional lines (e.g., RSI 40/60) are also available.
Glow Effects:
Switch between “Dynamic Transparency” (ATR-based) and “Direct Glow”.
Independently applied to the RSI 100 and RSI 0 lines.
EMAs (20/50, 100, 200):
Activate each one as needed.
Each EMA’s up/down color can be customized.
Example Use Cases
Trend Identification:
Enable Alligator lines to gauge general trend direction through SMMA signals.
Timing:
Watch the Candle Colors to spot potential overbought or oversold conditions.
Fine-Tuning:
Utilize the RSI lines to closely monitor important thresholds (50 as a trend barometer, 80/20 as possible reversal zones).
Filtering:
Enable a 50 EMA to quickly see if the market is trading above (bullish) or below (bearish) it.
AI Crypto Signals BTCUSD 15m Ultimate ScriptBYBIT:BTCUSD
Hello everyone! Sky First Capital in partnership with AI Crypto Signals is proud to introduce the AI Crypto Signals 15M BTCUSD Ultimate Script . This script works well on the 15M, 30M, 45M and 1HR chart using traditional candles. This means no false data or inaccurate entry/exit points such as with the ones using HA candles.
The script is based upon an initial strategy developed by user Bunghole here on TradingView, but we have optimized it, back-tested it with ideal settings, and added alerts that you can use to connect with your trading bot such as Alertatron, Cornix, etc. This script uses BB (Bollinger Bands) and RSI (Relative Strength Index) as indicators for signals.
Back-testing data for the 15M chart from 7/1/2021 to 10/15/2021 showed a 51.19% profit.
Back-testing data for the 1HR chart from 7/1/2021 to 10/15/2021 showed a 191% profit.
This script does not repaint.
Ideal use is to enter and exit at the close of the candle and take-profit/stop-loss once per candle.
This script has Entry/Exit/Take-Profit/Stop-Loss alerts.
We offer consulting and training services if you need help on using this script or getting it configured with an automated trading system.
We offer a 24 hour free trial of the script, send us a message to request access.
AI+ Scalper Strategy [BuBigMoneyMazz]Based on the AI+ Scalper Strategy
A trend-following swing strategy that uses multi-factor confirmation (trend, momentum, volatility) to capture sustained moves. Works best in trending markets and avoids choppy conditions using ADX filter.
🎯 5-Minute Chart Settings (Scalping)
pine
// RISK MANAGEMENT
ATR Multiplier SL: 1.2
ATR Multiplier TP: 2.4
// STRATEGY OPTIONS
Use HTF Filter: ON
HTF Timeframe: 15
Latching Mode: OFF
// INDICATOR SETTINGS
ADX Length: 10
ATR Length: 10
HMA Length: 14
Momentum Mode: Stochastic RSI
// STOCH RSI
Stoch RSI Length: 10
%K Smoothing: 2
%D Smoothing: 2
5-Minute Trading Style:
Quick scalps (15-45 minute holds)
Tight stops for fast markets
More frequent signals
Best during high volatility sessions (market open/close)
📈 15-Minute Chart Settings (Day Trading)
pine
// RISK MANAGEMENT
ATR Multiplier SL: 1.5
ATR Multiplier TP: 3.0
// STRATEGY OPTIONS
Use HTF Filter: ON
HTF Timeframe: 60
Latching Mode: ON
// INDICATOR SETTINGS
ADX Length: 14
ATR Length: 14
HMA Length: 21
Momentum Mode: Fisher RSI
// STOCH RSI
Stoch RSI Length: 12
%K Smoothing: 3
%D Smoothing: 3
15-Minute Trading Style:
Swing trades (1-4 hour holds)
Better risk-reward ratio
Fewer, higher quality signals
Works throughout trading day
⚡ Best Trading Times:
5-min: Market open (9:30-11:30 ET) & close (3:00-4:00 ET)
15-min: All day, but best 10:00-3:00 ET
✅ Filter for High-Probability Trades:
Only trade when ADX > 20 (strong trend)
Wait for HTF confirmation (prevents false signals)
Avoid low volume periods (lunch time)
⛔ When to Avoid Trading:
ADX < 15 (choppy market)
Major news events
First/last 15 minutes of session
Pro Tip: Start with 15-minute settings for better consistency, then move to 5-minute once you're comfortable with the strategy's behavior.
🏆 AI Gold Master IndicatorsAI Gold Master Indicators - Technical Overview
Core Purpose: Advanced Pine Script indicator that analyzes 20 technical indicators simultaneously for XAUUSD (Gold) trading, generating automated buy/sell signals through a sophisticated scoring system.
Key Features
📊 Multi-Indicator Analysis
Processes 20 indicators: RSI, MACD, Bollinger Bands, EMA crossovers, Stochastic, Williams %R, CCI, ATR, Volume, ADX, Parabolic SAR, Ichimoku, MFI, ROC, Fibonacci retracements, Support/Resistance, Candlestick patterns, MA Ribbon, VWAP, Market Structure, and Cloud MA
Each indicator generates BUY (🟢), SELL (🔴), or NEUTRAL (⚪) signals
⚖️ Dual Scoring Systems
Weighted System: Each indicator has configurable weights (10-200 points, total 1000), with higher weights for critical indicators like RSI (150) and MACD (150)
Simple Count System: Basic counting of BUY vs SELL signals across all indicators
🎯 Signal Generation
Configurable thresholds for both systems (weighted score threshold: 400-600 recommended)
Dynamic risk management with ATR-based TP/SL levels
Signal strength filtering to reduce false positives
📈 Advanced Configuration
Customizable thresholds for all 20 indicators (RSI levels, Stochastic bounds, Williams %R zones, etc.)
Dynamic weight bonuses that adapt to dominant market trends
Risk management with configurable TP1/TP2 multipliers and stop losses
🎛️ Visual Interface
Real-time master table displaying all indicators, their values, weights, and current signals
Visual trading signals (triangles) with detailed labels
Optional TP/SL lines and performance statistics
💡 Optimization Features
Gold-specific parameter tuning
Trend analysis with configurable lookback periods
Volume spike detection and volatility analysis
Multi-timeframe compatibility (15m, 1H, 4H recommended)
The system combines traditional technical analysis with modern weighting algorithms to provide comprehensive market analysis specifically optimized for gold trading.
Ragazzi è una meraviglia, pronto all uso, già configurato provatelo divertitevi e fate tanti soldoni poi magari una piccola donazione spontanea sarebbe molto gradita visto il tempo, risorse e gli insulti della moglie che mi diceva che perdevo tempo, fatemi sapere se vi piace.
nel codice troverete una descrizione del funzionamento se vi vengono in mente delle idee per migliorarlo contattatemi troverete i mie contatti in tabella un saluto.
AI BUY AND SELL BGThe Gk fundamental is a next gen level ai powered BUY and SELL system engineered for big market moves, it runs an embedded algorithm within a algorithm to detect breakout points before they happen giving traders insane results
works best and only 2h and 4h
AI-Powered Breakout with Advanced FeaturesDescription
This script is designed to detect breakout moments in financial markets using a combination of traditional breakout detection methods and adaptive moving averages. By leveraging elements of artificial intelligence, the script provides a more dynamic and responsive approach to identifying potential entry and exit points in trading.
Usefulness
This script stands out by integrating a traditional breakout finder with an adaptive moving average component. The adaptive moving average adjusts dynamically based on the differences between fast and slow exponential moving averages (EMAs), offering a more flexible and responsive detection of support and resistance levels. This combination aims to reduce false signals and enhance the reliability of breakout detections, making it a valuable tool for traders seeking to capture market movements more effectively.
Features
1. Breakout Detection: Utilizes pivot highs and lows to identify significant breakout points over a user-defined period. This method helps in capturing the essential support and resistance levels that are critical in breakout trading.
2. AI Machine Learning Component - Adaptive Moving Average: Implements an adaptive moving average using two exponential moving averages (EMAs). adaptiveMA is dynamically adjusted based on the difference between a fast average and a slow average.
3. Buy/Sell Signals: The script generates buy and sell signals when bullish and bearish breakouts occur, respectively. These signals are visually represented on the chart, helping traders to quickly identify potential trading opportunities.
4. Visualization: Draws horizontal lines at identified breakout levels and plots shapes (arrows) on the chart to indicate buy/sell signals. This makes it easy for traders to see where significant breakout points are and where to consider entering or exiting trades.
Underlying Concepts
1. Breakout Finder Logic: The script uses pivot points (highs and lows) to detect breakout levels. It stores these pivot points in arrays and monitors them for persistence, ensuring that the detected breakouts are significant and reliable.
2. Adaptive Moving Average (AMA): The AMA is a key component that enhances the script's responsiveness. By calculating the differences between fast and slow EMAs, the AMA adapts to changing market conditions, providing a more accurate measure of trends and potential reversals.
How to Use
• Adjustable Parameters: The script includes several user-adjustable parameters:
o Lookback Length: Defines the period over which the script calculates the highest high and lowest low for breakout detection.
o Multiplier for Adaptive MA: Adjusts the sensitivity of the adaptive moving average.
o Period for Pivots: Sets the period for detecting pivot highs and lows.
o Max Breakout Length: Specifies the maximum length for breakout consideration.
o Threshold Rate: Determines the threshold rate for breakout validation.
o Minimum Number of Tests: Sets the minimum number of tests required to validate a breakout.
o Colors and Line Style: Customize the colors and line styles for breakout levels.
Interpreting Signals
o Green Arrows: Indicate a bullish breakout signal, suggesting a potential buy opportunity.
o Red Arrows: Indicate a bearish breakout signal, suggesting a potential sell opportunity.
o Horizontal Lines: Show the breakout levels, helping to visualize support and resistance areas.
By combining traditional breakout detection with advanced adaptive moving averages, this script aims to provide traders with a robust tool for identifying and capitalizing on market breakouts.
Credits
Parts of this script were inspired and adapted from the "Breakout Finder" script by LonesomeTheBlue. Significant improvements include the integration of the adaptive moving average component and enhancements to the breakout detection logic.
AI SuperTrend - Strategy [presentTrading]
█ Introduction and How it is Different
The AI Supertrend Strategy is a unique hybrid approach that employs both traditional technical indicators and machine learning techniques. Unlike standard strategies that rely solely on traditional indicators or mathematical models, this strategy integrates the power of k-Nearest Neighbors (KNN), a machine learning algorithm, with the tried-and-true SuperTrend indicator. This blend aims to provide traders with more accurate, responsive, and context-aware trading signals.
*The KNN part is mainly referred from @Zeiierman.
BTCUSD 8hr performance
ETHUSD 8hr performance
█ Strategy, How it Works: Detailed Explanation
SuperTrend Calculation
Volume-Weighted Moving Average (VWMA): A VWMA of the close price is calculated based on the user-defined length (len). This serves as the central line around which the upper and lower bands are calculated.
Average True Range (ATR): ATR is calculated over a period defined by len. It measures the market's volatility.
Upper and Lower Bands: The upper band is calculated as VWMA + (factor * ATR) and the lower band as VWMA - (factor * ATR). The factor is a user-defined multiplier that decides how wide the bands should be.
KNN Algorithm
Data Collection: An array (data) is populated with recent n SuperTrend values. Corresponding labels (labels) are determined by whether the weighted moving average price (price) is greater than the weighted moving average of the SuperTrend (sT).
Distance Calculation: The absolute distance between each data point and the current SuperTrend value is calculated.
Sorting & Weighting: The distances are sorted in ascending order, and the closest k points are selected. Each point is weighted by the inverse of its distance to the current point.
Classification: A weighted sum of the labels of the k closest points is calculated. If the sum is closer to 1, the trend is predicted as bullish; if closer to 0, bearish.
Signal Generation
Start of Trend: A new bullish trend (Start_TrendUp) is considered to have started if the current trend color is bullish and the previous was not bullish. Similarly for bearish trends (Start_TrendDn).
Trend Continuation: A bullish trend (TrendUp) is considered to be continuing if the direction is negative and the KNN prediction is 1. Similarly for bearish trends (TrendDn).
Trading Logic
Long Condition: If Start_TrendUp or TrendUp is true, a long position is entered.
Short Condition: If Start_TrendDn or TrendDn is true, a short position is entered.
Exit Condition: Dynamic trailing stops are used for exits. If the trend does not continue as indicated by the KNN prediction and SuperTrend direction, an exit signal is generated.
The synergy between SuperTrend and KNN aims to filter out noise and produce more reliable trading signals. While SuperTrend provides a broad sense of the market direction, KNN refines this by predicting short-term price movements, leading to a more nuanced trading strategy.
Local picture
█ Trade Direction
The strategy allows traders to choose between taking only long positions, only short positions, or both. This is particularly useful for adapting to different market conditions.
█ Usage
ToolTips: Explains what each parameter does and how to adjust them.
Inputs: Customize values like the number of neighbors in KNN, ATR multiplier, and moving average type.
Plotting: Visual cues on the chart to indicate bullish or bearish trends.
Order Execution: Based on the generated signals, the strategy will execute buy/sell orders.
█ Default Settings
The default settings are selected to provide a balanced approach, but they can be modified for different trading styles and asset classes.
Initial Capital: $10,000
Default Quantity Type: 10% of equity
Commission: 0.1%
Slippage: 1
Currency: USD
By combining both machine learning and traditional technical analysis, this strategy offers a sophisticated and adaptive trading solution.
AI-Based Indicator V.1.01This is a Strategy based on Artificial Intelligence (AI) algorithms which can be used as a decision support system. In this version I use Heikin Ashi chart and reduce input parameters.
How to use:
1- Select the Heikin Ashi chart.
2- The default values of T for BTCUSD in "30m chart" is 0.12. It can be changed to achieve the best performance for BTCUSD or other tickers in arbitrary time frames.
3. When the background is green buy, and when the background is red sell.
AI-Based Strategy on Renko Chart V.1This is a Strategy based on Artificial Intelligence (AI) algorithms which can be used as a decision support system.
How to use:
1- Select the Renko chart.
2- Set "ATR Length" on settings window to "1". Settings can be seen after right click on the chart.
3- Use arbitrary time frame.
AI-Based Strategy V.1
This is a Strategy based on Artificial Intelligence (AI) algorithms which can be used (alone or along with other strategies) as a decision support system.
How to use:
1- The default values of Input 1, Input 2, R, and T for ETHUSDT are “Close”, “ohlc4”, 180, and 0.1325 respectively. They can be changed to achieve the best performance for ETHUSDT or other symbols.
2- Use one of the time frames 15 to 3m.
3. When the background is green buy, and when the background is red sell.
AI-Based Indicator V.1This is an indicator based on Artificial Intelligence (AI) algorithms which can be used (alone or along with other indicators) as a decision support system.
How to use:
1- The default values of Input 1, Input 2, R, and T for BTCUSD are “Close”, “Close”, 4320, and 0.15 respectively. They can be changed to achieve the best performance for BTCUSD or other tickers.
2- Use one of the time frames 4H to 15m.
3. When the background is green buy, and when the background is red sell.
