FxAST Lite Wave — Universal (Profiles: Intraday / Swing)FxAST-LW Universal (Profiles)
The FxAST Lite Wave – Universal strategy is designed for adaptability across markets and timeframes, with two ready-to-use profiles:
Intraday (5m–1H) → tuned for futures & FX scalps/day trades. Includes session filters, ATR volatility regimes, and impulse confirmation to reduce chop.
Swing (1D–3D) → tuned for swing positions. Uses relaxed impulse filters, slope + bias confirmation, and DI-spread to capture bigger moves.
Key features:
✅ Multi-EMA Lite Wave core (5/13/62/200)
✅ Regime filter via DI-spread (trend vs chop)
✅ EMA200 slope filter
✅ Optional HTF bias confirmation
✅ ATR-based stops, breakeven & trailing logic
✅ Time-stop exits to avoid capital stagnation
✅ Risk % position sizing
Usage:
Switch between Intraday and Swing modes via the Profile input. Adjust DI-spread, slope, and impulse thresholds per symbol. Sessions recommended ON for indices (NQ/ES/RTY) and OFF for FX.
⚠️ Disclaimer: This script is for research & educational purposes only. Not financial advice. Test extensively before applying live. Past performance does not guarantee future results.
© FxAST
Forecasting
Optimized ADX DI CCI Strategy### Key Features:
- Combines ADX, DI+/-, CCI, and RSI for signal generation.
- Supports customizable timeframes for indicators.
- Offers multiple exit conditions (Moving Average cross, ADX change, performance-based stop-loss).
- Tracks and displays trade statistics (e.g., win rate, capital growth, profit factor).
- Visualizes trades with labels and optional background coloring.
- Allows countertrading (opening an opposite trade after closing one).
1. **Indicator Calculation**:
- **ADX and DI+/-**: Calculated using the `ta.dmi` function with user-defined lengths for DI and ADX smoothing.
- **CCI**: Computed using the `ta.cci` function with a configurable source (default: `hlc3`) and length.
- **RSI (optional)**: Calculated using the `ta.rsi` function to filter overbought/oversold conditions.
- **Moving Averages**: Used for CCI signal smoothing and trade exits, with support for SMA, EMA, SMMA (RMA), WMA, and VWMA.
2. **Signal Generation**:
- **Buy Signal**: Triggered when DI+ > DI- (or DI+ crosses over DI-), CCI > MA (or CCI crosses over MA), and optional ADX/RSI filters are satisfied.
- **Sell Signal**: Triggered when DI+ < DI- (or DI- crosses over DI+), CCI < MA (or CCI crosses under MA), and optional ADX/RSI filters are satisfied.
3. **Trade Execution**:
- **Entry**: Long or short trades are opened using `strategy.entry` when signals are detected, provided trading is allowed (`allow_long`/`allow_short`) and equity is positive.
- **Exit**: Trades can be closed based on:
- Opposite signal (if no other exit conditions are used).
- MA cross (price crossing below/above the exit MA for long/short trades).
- ADX percentage change exceeding a threshold.
- Performance-based stop-loss (trade loss exceeding a percentage).
- **Countertrading**: If enabled, closing a trade triggers an opposite trade (e.g., closing a long opens a short).
4. **Visualization**:
- Labels are plotted at trade entries/exits (e.g., "BUY," "SELL," arrows).
- Optional background coloring highlights open trades (green for long, red for short).
- A statistics table displays real-time metrics (e.g., capital, win rates).
5. **Trade Tracking**:
- Tracks the number of long/short trades, wins, and overall performance.
- Monitors equity to prevent trading if it falls to zero.
### 2.3 Key Components
- **Indicator Calculations**: Uses `request.security` to fetch indicator data for the specified timeframe.
- **MA Function**: A custom `ma_func` handles different MA types for CCI and exit conditions.
- **Signal Logic**: Combines crossover/under checks with recent bar windows for flexibility.
- **Exit Conditions**: Multiple configurable exit strategies for risk management.
- **Statistics Table**: Updates dynamically with trade and capital metrics.
## 3. Configuration Options
The script provides extensive customization through input parameters, grouped for clarity in the TradingView settings panel. Below is a detailed breakdown of each setting and its impact.
### 3.1 Strategy Settings (Global)
- **Initial Capital**: Default `10000`. Sets the starting capital for backtesting.
- **Effect**: Determines the base equity for calculating position sizes and performance metrics.
- **Default Quantity Type**: `strategy.percent_of_equity` (50% of equity).
- **Effect**: Controls the size of each trade as a percentage of available equity.
- **Pyramiding**: Default `2`. Allows up to 2 simultaneous trades in the same direction.
- **Effect**: Enables multiple entries if conditions are met, increasing exposure.
- **Commission**: 0.2% per trade.
- **Effect**: Simulates trading fees, reducing net profit in backtesting.
- **Margin**: 100% for long and short trades.
- **Effect**: Assumes no leverage; adjust for margin trading simulations.
- **Calc on Every Tick**: `true`.
- **Effect**: Ensures real-time signal updates for precise execution.
### 3.2 Indicator Settings
- **Indicator Timeframe** (`indicator_timeframe`):
- **Options**: `""` (chart timeframe), `1`, `5`, `15`, `30`, `60`, `240`, `D`, `W`.
- **Default**: `""` (uses chart timeframe).
- **Effect**: Determines the timeframe for ADX, DI, CCI, and RSI calculations. A higher timeframe reduces noise but may delay signals.
### 3.3 ADX & DI Settings
- **DI Length** (`adx_di_len`):
- **Default**: `30`.
- **Range**: Minimum `1`.
- **Effect**: Sets the period for calculating DI+ and DI-. Longer periods smooth trends but reduce sensitivity.
- **ADX Smoothing Length** (`adx_smooth_len`):
- **Default**: `14`.
- **Range**: Minimum `1`.
- **Effect**: Smooths the ADX calculation. Longer periods produce smoother ADX values.
- **Use ADX Filter** (`use_adx_filter`):
- **Default**: `false`.
- **Effect**: If `true`, requires ADX to exceed the threshold for signals to be valid, filtering out weak trends.
- **ADX Threshold** (`adx_threshold`):
- **Default**: `25`.
- **Range**: Minimum `0`.
- **Effect**: Sets the minimum ADX value for valid signals when the filter is enabled. Higher values restrict trades to stronger trends.
### 3.4 CCI Settings
- **CCI Length** (`cci_length`):
- **Default**: `20`.
- **Range**: Minimum `1`.
- **Effect**: Sets the period for CCI calculation. Longer periods reduce noise but may lag.
- **CCI Source** (`cci_src`):
- **Default**: `hlc3` (average of high, low, close).
- **Effect**: Defines the price data for CCI. `hlc3` is standard, but users can choose other sources (e.g., `close`).
- **CCI MA Type** (`ma_type`):
- **Options**: `SMA`, `EMA`, `SMMA (RMA)`, `WMA`, `VWMA`.
- **Default**: `SMA`.
- **Effect**: Determines the moving average type for CCI signal smoothing. EMA is more responsive; VWMA weights by volume.
- **CCI MA Length** (`ma_length`):
- **Default**: `14`.
- **Range**: Minimum `1`.
- **Effect**: Sets the period for the CCI MA. Longer periods smooth the MA but may delay signals.
### 3.5 RSI Filter Settings
- **Use RSI Filter** (`use_rsi_filter`):
- **Default**: `false`.
- **Effect**: If `true`, applies RSI-based overbought/oversold filters to signals.
- **RSI Length** (`rsi_length`):
- **Default**: `14`.
- **Range**: Minimum `1`.
- **Effect**: Sets the period for RSI calculation. Longer periods reduce sensitivity.
- **RSI Lower Limit** (`rsi_lower_limit`):
- **Default**: `30`.
- **Range**: `0` to `100`.
- **Effect**: Defines the oversold threshold for buy signals. Lower values allow trades in more extreme conditions.
- **RSI Upper Limit** (`rsi_upper_limit`):
- **Default**: `70`.
- **Range**: `0` to `100`.
- **Effect**: Defines the overbought threshold for sell signals. Higher values allow trades in more extreme conditions.
### 3.6 Signal Settings
- **Cross Window** (`cross_window`):
- **Default**: `0`.
- **Range**: `0` to `5` bars.
- **Effect**: Specifies the lookback period for detecting DI+/- or CCI crosses. `0` requires crosses on the current bar; higher values allow recent crosses, increasing signal frequency.
- **Allow Long Trades** (`allow_long`):
- **Default**: `true`.
- **Effect**: Enables/disables new long trades. If `false`, only closing existing longs is allowed.
- **Allow Short Trades** (`allow_short`):
- **Default**: `true`.
- **Effect**: Enables/disables new short trades. If `false`, only closing existing shorts is allowed.
- **Require DI+/DI- Cross for Buy** (`buy_di_cross`):
- **Default**: `true`.
- **Effect**: If `true`, requires a DI+ crossover DI- for buy signals; if `false`, DI+ > DI- is sufficient.
- **Require CCI Cross for Buy** (`buy_cci_cross`):
- **Default**: `true`.
- **Effect**: If `true`, requires a CCI crossover MA for buy signals; if `false`, CCI > MA is sufficient.
- **Require DI+/DI- Cross for Sell** (`sell_di_cross`):
- **Default**: `true`.
- **Effect**: If `true`, requires a DI- crossover DI+ for sell signals; if `false`, DI+ < DI- is sufficient.
- **Require CCI Cross for Sell** (`sell_cci_cross`):
- **Default**: `true`.
- **Effect**: If `true`, requires a CCI crossunder MA for sell signals; if `false`, CCI < MA is sufficient.
- **Countertrade** (`countertrade`):
- **Default**: `true`.
- **Effect**: If `true`, closing a trade triggers an opposite trade (e.g., close long, open short) if allowed.
- **Color Background for Open Trades** (`color_background`):
- **Default**: `true`.
- **Effect**: If `true`, colors the chart background green for long trades and red for short trades.
### 3.7 Exit Settings
- **Use MA Cross for Exit** (`use_ma_exit`):
- **Default**: `true`.
- **Effect**: If `true`, closes trades when the price crosses the exit MA (below for long, above for short).
- **MA Length for Exit** (`ma_exit_length`):
- **Default**: `20`.
- **Range**: Minimum `1`.
- **Effect**: Sets the period for the exit MA. Longer periods delay exits.
- **MA Type for Exit** (`ma_exit_type`):
- **Options**: `SMA`, `EMA`, `SMMA (RMA)`, `WMA`, `VWMA`.
- **Default**: `SMA`.
- **Effect**: Determines the MA type for exit signals. EMA is more responsive; VWMA weights by volume.
- **Use ADX Change Stop-Loss** (`use_adx_stop`):
- **Default**: `false`.
- **Effect**: If `true`, closes trades when the ADX changes by a specified percentage.
- **ADX % Change for Stop-Loss** (`adx_change_percent`):
- **Default**: `5.0`.
- **Range**: Minimum `0.0`, step `0.1`.
- **Effect**: Specifies the percentage change in ADX (vs. previous bar) that triggers a stop-loss. Higher values reduce premature exits.
- **Use Performance Stop-Loss** (`use_perf_stop`):
- **Default**: `false`.
- **Effect**: If `true`, closes trades when the loss exceeds a percentage threshold.
- **Performance Stop-Loss (%)** (`perf_stop_percent`):
- **Default**: `-10.0`.
- **Range**: `-100.0` to `0.0`, step `0.1`.
- **Effect**: Specifies the loss percentage that triggers a stop-loss. More negative values allow larger losses before exiting.
## 4. Visual and Statistical Output
- **Labels**: Displayed at trade entries/exits with arrows (↑ for buy, ↓ for sell) and text ("BUY," "SELL"). A "No Equity" label appears if equity is zero.
- **Background Coloring**: Optionally colors the chart background (green for long, red for short) to indicate open trades.
- **Statistics Table**: Displayed at the top center of the chart, updated on timeframe changes or trade events. Includes:
- **Capital Metrics**: Initial capital, current capital, capital growth (%).
- **Trade Metrics**: Total trades, long/short trades, win rate, long/short win rates, profit factor.
- **Open Trade Status**: Indicates if a long, short, or no trade is open.
## 5. Alerts
- **Buy Signal Alert**: Triggered when `buy_signal` is true ("Cross Buy Signal").
- **Sell Signal Alert**: Triggered when `sell_signal` is true ("Cross Sell Signal").
- **Usage**: Users can set up TradingView alerts to receive notifications for trade signals.
EUR/USD Multi-Layer Statistical Regression StrategyStrategy Overview
This advanced EUR/USD trading system employs a triple-layer linear regression framework with statistical validation and ensemble weighting. It combines short, medium, and long-term regression analyses to generate high-confidence directional signals while enforcing strict risk controls.
Core Components
Multi-Layer Regression Engine:
Parallel regression analysis across 3 customizable timeframes (short/medium/long)
Projects future price values using prediction horizons
Statistical significance filters (R-squared, correlation, slope thresholds)
Signal Validation System:
Lookback validation tests historical prediction accuracy
Ensemble weighting of layer signals (adjustable influence per timeframe)
Confidence scoring combining statistical strength, layer agreement, and validation accuracy
Risk Management:
Position sizing scaled by signal confidence (1%-100% of equity)
Daily loss circuit breaker (halts trading at user-defined threshold)
Forex-tailored execution (pip slippage, percentage-based commissions)
Visual Intelligence:
Real-time regression line plots (3 layered colors)
Projection markers for short-term forecasts
Background coloring for market bias indication
Comprehensive statistics dashboard (R-squared metrics, validation scores, P&L)
Key Parameters
Category Settings
Regression Short/Med/Long lengths (20/50/100 bars)
Statistics Min R² (0.65), Correlation (0.7), Slope (0.0001)
Validation 30-bar lookback, 10-bar projection
Risk Controls 50% position size, 12% daily loss limit, 75% confidence threshold
Trading Logic
Entries require:
Ensemble score > |0.5|
Confidence > threshold
Short & medium-term significance
Active daily loss limit not breached
Exits triggered by:
Opposite high-confidence signals
Daily loss limit violation (emergency exit)
The strategy blends quantitative finance techniques with practical trading safeguards, featuring a self-optimizing design where signal quality directly impacts position sizing. The visual dashboard provides real-time feedback on model performance and market conditions.
Long and Short Strategy with Multi Indicators [B1P5]Long and Short Strategy with RSI, ROC, MA Selection, Exit Visualization, and Strength Indicator
Enhanced Market Structure StrategyATR-Based Risk Management:
Stop Loss: 2 ATR from entry (configurable)
Take Profit: 3 ATR from entry (configurable)
Dynamic Position Sizing: Based on ATR stop distance and max risk percentage
Advanced Signal Filters:
RSI Filter:
Long trades: RSI < 70 and > 40 (avoiding overbought)
Short trades: RSI > 30 and < 60 (avoiding oversold)
Volume Filter:
Requires volume > 1.2x the 20-period moving average
Ensures institutional participation
MACD Filter (Optional):
Long: MACD line above signal line and rising
Short: MACD line below signal line and falling
EMA Trend Filter:
50-period EMA for trend confirmation
Long trades require price above rising EMA
Short trades require price below falling EMA
Higher Timeframe Filter:
Uses 4H/Daily EMA for multi-timeframe confluence
Enhanced Entry Logic:
Regular Entries: IDM + BOS + ALL filters must pass
Sweep Entries: Failed breakouts with tighter stops (1.6 ATR)
High-Probability Focus: Only trades when multiple confirmations align
Visual Improvements:
Detailed Entry Labels: Show entry, stop, target, and risk percentage
SL/TP Lines: Visual representation of risk/reward
Filter Status: Bar coloring shows when all filters align
Comprehensive Statistics: Real-time performance metrics
Key Strategy Parameters:
pinescript// Recommended Settings for Different Markets:
// Forex (4H-Daily):
// - CHoCH Period: 50-75
// - ATR SL: 2.0, ATR TP: 3.0
// - All filters enabled
// Crypto (1H-4H):
// - CHoCH Period: 30-50
// - ATR SL: 2.5, ATR TP: 4.0
// - Volume filter especially important
// Indices (4H-Daily):
// - CHoCH Period: 50-100
// - ATR SL: 1.8, ATR TP: 2.7
// - EMA and MACD filters crucial
Expected Performance Improvements:
Win Rate: 55-70% (improved filtering)
Profit Factor: 2.0-3.5+ (better risk/reward with ATR)
Reduced Drawdown: Stricter filters reduce false signals
Consistent Risk: ATR-based stops adapt to volatility
This enhanced version provides much more robust signal filtering while maintaining the core market structure edge, resulting in higher-probability trades with consistent risk management.
LANZ Strategy 6.0 [Backtest]🔷 LANZ Strategy 6.0 — Precision Backtesting Based on 09:00 NY Candle, Dynamic SL/TP, and Lot Size per Trade
LANZ Strategy 6.0 is the simulation version of the original LANZ 6.0 indicator. It executes a single LIMIT BUY order per day based on the 09:00 a.m. New York candle, using dynamic Stop Loss and Take Profit levels derived from the candle range. Position sizing is calculated automatically using capital, risk percentage, and pip value — allowing accurate trade simulation and performance tracking.
📌 This is a strategy script — It simulates real trades using strategy.entry() and strategy.exit() with full money management for risk-based backtesting.
🧠 Core Logic & Trade Conditions
🔹 BUY Signal Trigger:
At 09:00 a.m. NY (New York time), if:
The current candle is bullish (close > open)
→ A BUY order is placed at the candle’s close price (EP)
Only one signal is evaluated per day.
⚙️ Stop Loss / Take Profit Logic
SL can be:
Wick low (0%)
Or dynamically calculated using a % of the full candle range
TP is calculated using the user-defined Risk/Reward ratio (e.g., 1:4)
The TP and SL levels are passed to strategy.exit() for each trade simulation.
💰 Risk Management & Lot Size Calculation
Before placing the trade:
The system calculates pip distance from EP to SL
Computes the lot size based on:
Account capital
Risk % per trade
Pip value (auto or manual)
This ensures every trade uses consistent, scalable risk regardless of instrument.
🕒 Manual Close at 3:00 p.m. NY
If the trade is still open by 15:00 NY time, it will be closed using strategy.close().
The final result is the actual % gain/loss based on how far price moved relative to SL.
📊 Backtest Accuracy
One trade per day
LIMIT order at the candle close
SL and TP pre-defined at execution
No repainting
Session-restricted (only runs on 1H timeframe)
✅ Ideal For:
Traders who want to backtest a clean and simple daily entry system
Strategy developers seeking reproducible, high-conviction trades
Users who prefer non-repainting, session-based simulations
👨💻 Credits:
💡 Developed by: LANZ
🧠 Logic & Money Management Engine: LANZ
📈 Designed for: 1H charts
🧪 Purpose: Accurate simulation of LANZ 6.0's NY Candle Entry system
RSI Divergence StrategyOverview
The RSI Divergence Strategy Indicator is a trading tool that uses the RSI and divergences created to generate high-probability buy and sell signals.
I have provided the best formula of numbers to use for BTC on a 30 minute timeframe.
You can change where on RSI you enter and exit both long or short trades. This way you can experiment on different tokens using different entry/exit points. Can use on multiple timeframes.
This strategy is designed to open and close long or short trades based on the levels you provide it. You can then check on the RSI where the best levels are for each token you want to trade and amend it as required to generate a profitable strategy.
How It Works
The RSI Divergence Strategy Indicator uses bear and bull divergences in conjuction with a level you have input on the RSI.
RSI for Overbought/Oversold:
• Input variables for entry and exit levels and when the entry levels combine with a bear or bull divergence signal, a trade is alerted.
RSI Divergence:
• Buy and sell signals are confirmed when the RSI creates bearish or bullish divergences and these divergences are in the same area as your levels you input for entry to short or long.
After 7 years of experience and testing I have calculated the exact numbers required and produced a formula to calculate the exact input variables for a 30 minute Bitcoin chart.
Key Features
1️⃣ Divergence Identification – Ensures trades are taken only when a bull or bear divergence has formed.
2️⃣ Overbought/Oversold Input Filtering – Set up your own variables on the RSI for different markets after identifying patterns on the RSI in relation to a bearish or bullish divergence.
3️⃣ Works on any chart – Suitable for all markets and timeframes once you input the correct variables for entry and exit levels.
How to Use
🟢 Basic Trading:
• Use on any timeframe.
• Enter trade only when alert has fired off. Close when it says to exit.
• Change entry and exit levels in the properties of the strategy indicator.
• Make entry and exit levels coincide with bearish or bullish divergences on the RSI.
Check the strategy tester to see backtesting so you know if the indicator is profitable or not for that market and timeframe as each crypto token is different and so is the timeframe you choose.
📢 Webhook Automation:
• Set up TradingView Alerts to auto-execute trades via Webhook-compatible platforms.
Key additions for divergence visualization:
Divergence Arrows:
Bullish divergence: Green label with white 'bull ' text
Bearish divergence: Red label with white 'bear' text
Positioned at the pivot point
Divergence Lines:
Connects consecutive RSI pivot points
Automatically drawn between consecutive pivot points
Enhanced RSI Coloring:
Overbought zone: Red
Oversold zone: Green
Neutral zone: Gray
The visualization helps you instantly spot:
Where divergences are forming on the RSI
The pattern of higher lows (bullish) or lower highs (bearish)
Contextual coloring of RSI relative to standard levels
All divergence markers appear at the correct historical pivot points, making it easy to visually confirm divergence patterns as they develop.
Strategy levels and background zones also shown to help visual look.
Why This Combination?
This indicator is just a simple RSI tool.
It is designed to filter out weak trades and only execute trades that have:
✅ RSI Divergence
✅ Overbought or Oversold Conditions
It does not calculate downtrends or bear markets so care is recommended taking long trades during these times.
Why It’s Worth Using?
📈 Open Source – Free to use and learn from.
📉 Long or Short Term Trading Style – Entry/Exit parameters options are designed for both short or long term trades allowing you to experiment until you find a profitable strategy for that market you want to trade.
📢 Seamless Webhook Automation – Execute trades automatically with TradingView alerts.
💲 Ready to trade smarter?
✅ Add the RSI Divergence Strategy Indicator to your TradingView chart.
Price Statistical Strategy-Z Score V 1.01
Price Statistical Strategy – Z Score V 1.01
Overview
A technical breakdown of the logic and components of the “Price Statistical Strategy – Z Score V 1.01”.
This script implements a smoothed Z-Score crossover mechanism applied to the closing price to detect potential statistical deviations from local price mean. The strategy operates solely on price data (close) and includes signal spacing control and momentum-based candle filters. No volume-based or trend-detection components are included.
Core Methodology
The strategy is built on the statistical concept of Z-Score, which quantifies how far a value (closing price) is from its recent average, normalized by standard deviation. Two moving averages of the raw Z-Score are calculated: a short-term and a long-term smoothed version. The crossover between them generates long entries and exits.
Signal Conditions
Entry Condition:
A long position is opened when the short-term smoothed Z-Score crosses above the long-term smoothed Z-Score, and additional entry conditions are met.
Exit Condition:
The position is closed when the short-term Z-Score crosses below the long-term Z-Score, provided the exit conditions allow.
Signal Gapping:
A minimum number of bars (Bars gap between identical signals) must pass between repeated entry or exit signals to reduce noise.
Momentum Filter:
Entries are prevented during sequences of three or more consecutively bullish candles, and exits are prevented during three or more consecutively bearish candles.
Z-Score Function
The Z-Score is calculated as:
Z = (Close - SMA(Close, N)) / STDEV(Close, N)
Where N is the base period selected by the user.
Input Parameters
Enable Smoothed Z-Score Strategy
Enables or disables the Z-Score strategy logic. When disabled, no trades are executed.
Z-Score Base Period
Defines the number of bars used to calculate the simple moving average and standard deviation for the Z-Score. This value affects how responsive the raw Z-Score is to price changes.
Short-Term Smoothing
Sets the smoothing window for the short-term Z-Score. Higher values produce smoother short-term signals, reducing sensitivity to short-term volatility.
Long-Term Smoothing
Sets the smoothing window for the long-term Z-Score, which acts as the reference line in the crossover logic.
Bars gap between identical signals
Minimum number of bars that must pass before another signal of the same type (entry or exit) is allowed. This helps reduce redundant or overly frequent signals.
Trade Visualization Table
A table positioned at the bottom-right displays live PnL for open trades:
Entry Price
Unrealized PnL %
Text colors adapt based on whether unrealized profit is positive, negative, or neutral.
Technical Notes
This strategy uses only close prices — no trend indicators or volume components are applied.
All calculations are based on simple moving averages and standard deviation over user-defined windows.
Designed as a minimal, isolated Z-Score engine without confirmation filters or multi-factor triggers.
Timeframe StrategyThis is a multi-timeframe trading strategy inspired by Ross Cameron's style, optimized for scalping and trend-following across various timeframes (1m, 5m, 15m, 1h, and 1D). The strategy integrates a comprehensive set of technical indicators, dynamic risk management, and visual tools.
Core Features
Dynamic Take Profit, Stop Loss & Trailing Stop
> Separate settings per timeframe for:
-TP% (Take Profit)
-SL% (Stop Loss)
-Trailing Stop %
-Cooldown bars
> Configurable via UI inputs.
>Smart Entry Conditions
Bullish entry: EMA9 crossover EMA20 and EMA50 > EMA200
Bearish entry: EMA9 crossunder EMA20 and EMA50 < EMA200
>Additional confirmation filters:
-Volume Filter (enabled/disabled via UI)
-Time Filter (e.g., only between 15:00–20:00 UTC)
-Spike Filter: rejects high-volatility candles
-RSI Filter: above/below 50 for trend confirmation
-ADX Filter (only applied on 1m, e.g., ADX > 15)
-Micro-Volatility Filter: minimum range percentage (1m only)
-Trend Filter (1m only): price must be above/below EMA200
>Trailing Stop Logic
-Configurable for each timeframe.
- Optional via toggle (use_trailing).
>Trade Cooldown Logic
-Prevents consecutive trades within X bars, configurable per timeframe.
>Technical Indicators Used
-EMA 9 / 20 / 50 / 200
-VWAP
-RSI (14)
-ATR (14) for volatility-based spike filtering
-Custom-calculated ADX (14) (manually implemented)
>Visual Elements
🔼/🔽 Entry signals (long/short) plotted on the chart.
📉 Table in bottom-left:
Displays current values of EMA/VWAP/volume/ATR/ADX.
> Optional "Tab info" panel in top-right (toggleable):
-Timeframe & strategy settings
-Live status of filters (volume, time, cooldown, spike, RSI, ADX, range, trend)
-Uses emoji (✅ / ❌) for quick diagnostics.
>User Customization
-Inputs per timeframe for all key parameters.
-Toggle switches for:
-Trailing stop
-Volume filter
-Info table visibility
This strategy is designed for active traders seeking a balance between momentum entry, risk control, and adaptability across timeframes. It's ideal for backtesting quick reversals or breakout setups in fast markets, especially at lower timeframes like 1m or 5m.
30-70 RSI Strategy with Colored BarThis script colors price bars based on Relative Strength Index (RSI) levels, giving traders a quick and visual way to assess overbought or oversold market conditions directly on the chart.
📈 Key Features:
✅ RSI-Based Bar Coloring:
Green bars when RSI is above the upper threshold (default 70) – suggests bullish momentum.
Red bars when RSI is below the lower threshold (default 30) – indicates bearish pressure.
Bars remain uncolored when RSI is between thresholds – a neutral zone.
🔧 Customizable RSI Settings:
Adjustable RSI length (default: 14 periods)
Adjustable overbought/oversold levels (default: 70/30)
🧠 Helps traders:
Quickly spot potential reversals or trend continuations
Visually align price action with momentum
🛠️ Usage:
Ideal for trend-following, reversal, and momentum strategies.
Works across any timeframe (1m, 5m, 1h, daily, etc.).
Sharpe Ratio Forced Selling StrategyThis study introduces the “Sharpe Ratio Forced Selling Strategy”, a quantitative trading model that dynamically manages positions based on the rolling Sharpe Ratio of an asset’s excess returns relative to the risk-free rate. The Sharpe Ratio, first introduced by Sharpe (1966), remains a cornerstone in risk-adjusted performance measurement, capturing the trade-off between return and volatility. In this strategy, entries are triggered when the Sharpe Ratio falls below a specified low threshold (indicating excessive pessimism), and exits occur either when the Sharpe Ratio surpasses a high threshold (indicating optimism or mean reversion) or when a maximum holding period is reached.
The underlying economic intuition stems from institutional behavior. Institutional investors, such as pension funds and mutual funds, are often subject to risk management mandates and performance benchmarking, requiring them to reduce exposure to assets that exhibit deteriorating risk-adjusted returns over rolling periods (Greenwood and Scharfstein, 2013). When risk-adjusted performance improves, institutions may rebalance or liquidate positions to meet regulatory requirements or internal mandates, a behavior that can be proxied effectively through a rising Sharpe Ratio.
By systematically monitoring the Sharpe Ratio, the strategy anticipates when “forced selling” pressure is likely to abate, allowing for opportunistic entries into assets priced below fundamental value. Exits are equally mechanized, either triggered by Sharpe Ratio improvements or by a strict time-based constraint, acknowledging that institutional rebalancing and window-dressing activities are often time-bound (Coval and Stafford, 2007).
The Sharpe Ratio is particularly suitable for this framework due to its ability to standardize excess returns per unit of risk, ensuring comparability across timeframes and asset classes (Sharpe, 1994). Furthermore, adjusting returns by a dynamically updating short-term risk-free rate (e.g., US 3-Month T-Bills from FRED) ensures that macroeconomic conditions, such as shifting interest rates, are accurately incorporated into the risk assessment.
While the Sharpe Ratio is an efficient and widely recognized measure, the strategy could be enhanced by incorporating alternative or complementary risk metrics:
• Sortino Ratio: Unlike the Sharpe Ratio, the Sortino Ratio penalizes only downside volatility (Sortino and van der Meer, 1991). This would refine entries and exits to distinguish between “good” and “bad” volatility.
• Maximum Drawdown Constraints: Integrating a moving window maximum drawdown filter could prevent entries during persistent downtrends not captured by volatility alone.
• Conditional Value at Risk (CVaR): A measure of expected shortfall beyond the Value at Risk, CVaR could further constrain entry conditions by accounting for tail risk in extreme environments (Rockafellar and Uryasev, 2000).
• Dynamic Thresholds: Instead of static Sharpe thresholds, one could implement dynamic bands based on the historical distribution of the Sharpe Ratio, adjusting for volatility clustering effects (Cont, 2001).
Each of these risk parameters could be incorporated into the current script as additional input controls, further tailoring the model to different market regimes or investor risk appetites.
References
• Cont, R. (2001) ‘Empirical properties of asset returns: stylized facts and statistical issues’, Quantitative Finance, 1(2), pp. 223-236.
• Coval, J.D. and Stafford, E. (2007) ‘Asset Fire Sales (and Purchases) in Equity Markets’, Journal of Financial Economics, 86(2), pp. 479-512.
• Greenwood, R. and Scharfstein, D. (2013) ‘The Growth of Finance’, Journal of Economic Perspectives, 27(2), pp. 3-28.
• Rockafellar, R.T. and Uryasev, S. (2000) ‘Optimization of Conditional Value-at-Risk’, Journal of Risk, 2(3), pp. 21-41.
• Sharpe, W.F. (1966) ‘Mutual Fund Performance’, Journal of Business, 39(1), pp. 119-138.
• Sharpe, W.F. (1994) ‘The Sharpe Ratio’, Journal of Portfolio Management, 21(1), pp. 49-58.
• Sortino, F.A. and van der Meer, R. (1991) ‘Downside Risk’, Journal of Portfolio Management, 17(4), pp. 27-31.
Dkoderweb repainting issue fix strategyHarmonic Pattern Recognition Trading Strategy
This TradingView strategy called "Dkoderweb repainting issue fix strategy" is designed to identify and trade harmonic price patterns with optimized entry and exit points using Fibonacci levels. The strategy implements various popular harmonic patterns including Bat, Butterfly, Gartley, Crab, Shark, ABCD, and their anti-patterns.
Key Features
Pattern Recognition: Identifies 17+ harmonic price patterns including standard and anti-patterns
Fibonacci-Based Entries and Exits: Uses customizable Fibonacci levels for precision entries, take profits, and stop losses
Alternative Timeframe Analysis: Option to use higher timeframes for pattern identification
Heiken Ashi Support: Optional use of Heiken Ashi candles instead of regular candlesticks
Visual Indicators:
Pattern visualization with ZigZag indicator
Buy/sell signal markers
Color-coded background to highlight active trade zones
Customizable Fibonacci level display
How It Works
The strategy uses a ZigZag-based pattern identification system to detect pivot points
When a valid harmonic pattern forms, the strategy calculates the optimal entry window using the specified Fibonacci level (default 0.382)
Entries trigger when price returns to the entry window after pattern completion
Take profit and stop loss levels are automatically set based on customizable Fibonacci ratios
Visual alerts notify you of entries and exits
The strategy tracks active trades and displays them with background color highlights
Customizable Settings
Trade size
Entry window Fibonacci level (default 0.382)
Take profit Fibonacci level (default 0.618)
Stop loss Fibonacci level (default -0.618)
Alert messages for entries and exits
Display options for specific Fibonacci levels
Alternative timeframe selection
This strategy is designed to fix repainting issues that are common in harmonic pattern strategies, ensuring more reliable signals and backtesting results.
IU Bigger than range strategyDESCRIPTION
IU Bigger Than Range Strategy is designed to capture breakout opportunities by identifying candles that are significantly larger than the previous range. It dynamically calculates the high and low of the last N candles and enters trades when the current candle's range exceeds the previous range. The strategy includes multiple stop-loss methods (Previous High/Low, ATR, Swing High/Low) and automatically manages take-profit and stop-loss levels based on user-defined risk-to-reward ratios. This versatile strategy is optimized for higher timeframes and assets like BTC but can be fine-tuned for different instruments and intervals.
USER INPUTS:
Look back Length: Number of candles to calculate the high-low range. Default is 22.
Risk to Reward: Sets the target reward relative to the stop-loss distance. Default is 3.
Stop Loss Method: Choose between:(Default is "Previous High/Low")
- Previous High/Low
- ATR (Average True Range)
- Swing High/Low
ATR Length: Defines the length for ATR calculation (only applicable when ATR is selected as the stop-loss method) (Default is 14).
ATR Factor: Multiplier applied to the ATR to determine stop-loss distance(Default is 2).
Swing High/Low Length: Specifies the length for identifying swing points (only applicable when Swing High/Low is selected as the stop-loss method).(Default is 2)
LONG CONDITION:
The current candle’s range (absolute difference between open and close) is greater than the previous range.
The closing price is higher than the opening price (bullish candle).
SHORT CONDITIONS:
The current candle’s range exceeds the previous range.
The closing price is lower than the opening price (bearish candle).
LONG EXIT:
Stop-loss:
- Previous Low
- ATR-based trailing stop
- Recent Swing Low
Take-profit:
- Defined by the Risk-to-Reward ratio (default 3x the stop-loss distance).
SHORT EXIT:
Stop-loss:
- Previous High
- ATR-based trailing stop
- Recent Swing High
Take-profit:
- Defined by the Risk-to-Reward ratio (default 3x the stop-loss distance).
ALERTS:
Long Entry Triggered
Short Entry Triggered
WHY IT IS UNIQUE:
This strategy dynamically adapts to different market conditions by identifying candles that exceed the previous range, ensuring that it only enters trades during strong breakout scenarios.
Multiple stop-loss methods provide flexibility for different trading styles and risk profiles.
The visual representation of stop-loss and take-profit levels with color-coded plots improves trade monitoring and decision-making.
HOW USERS CAN BENEFIT FROM IT:
Ideal for breakout traders looking to capitalize on momentum-driven price moves.
Provides flexibility to customize stop-loss methods and fine-tune risk management parameters.
Helps minimize drawdowns with a strong risk-to-reward framework while maximizing profit potential.
IU BBB(Big Body Bar) StrategyDESCRIPTION
The IU BBB (Big Body Bar) Strategy is a price action-based trading strategy that identifies high-momentum candles with significantly larger body sizes compared to the average. It enters trades when a strong bullish or bearish move occurs and manages risk using an ATR-based trailing stop-loss system.
USER INPUTS:
- Big Body Threshold – Defines how many times larger the candle body should be compared to the average body ( default is 4 ).
- ATR Length – The period for the Average True Range (ATR) used in the trailing stop-loss calculation ( default is 14 ).
- ATR Factor – Multiplier for ATR to determine the trailing stop distance ( default is 2 ).
LONG CONDITION:
- The current candle’s body is greater than the average body size multiplied by the Big Body Threshold.
- The closing price is higher than the opening price (bullish candle).
SHORT CONDITION:
- The current candle’s body is greater than the average body size multiplied by the Big Body Threshold.
- The closing price is lower than the opening price (bearish candle).
LONG EXIT:
- ATR-based trailing stop-loss dynamically adjusts, locking in profits as the price moves higher.
SHORT EXIT:
- ATR-based trailing stop-loss dynamically adjusts, securing profits as the price moves lower.
WHY IT IS UNIQUE:
- Unlike traditional momentum strategies, this system adapts to volatility by filtering trades based on relative candle size.
- It incorporates an ATR-based trailing stop-loss, ensuring risk management and profit protection.
- The strategy avoids choppy market conditions by only trading when significant momentum is present.
HOW USERS CAN BENEFIT FROM IT:
- Catch Strong Price Moves – The strategy helps traders enter trades when the market shows decisive momentum.
- Effective Risk Management – The ATR-based trailing stop ensures that winning trades remain profitable.
- Works Across Markets – Can be applied to stocks, forex, crypto, and indices with proper optimization.
- Fully Customizable – Users can adjust sensitivity settings to match their trading style and time frame.
Simple APF Strategy Backtesting [The Quant Science]Simple backtesting strategy for the quantitative indicator Autocorrelation Price Forecasting. This is a Buy & Sell strategy that operates exclusively with long orders. It opens long positions and generates profit based on the future price forecast provided by the indicator. It's particularly suitable for trend-following trading strategies or directional markets with an established trend.
Main functions
1. Cycle Detection: Utilize autocorrelation to identify repetitive market behaviors and cycles.
2. Forecasting for Backtesting: Simulate trades and assess the profitability of various strategies based on future price predictions.
Logic
The strategy works as follow:
Entry Condition: Go long if the hypothetical gain exceeds the threshold gain (configurable by user interface).
Position Management: Sets a take-profit level based on the future price.
Position Sizing: Automatically calculates the order size as a percentage of the equity.
No Stop-Loss: this strategy doesn't includes any stop loss.
Example Use Case
A trader analyzes a dayli period using 7 historical bars for autocorrelation.
Sets a threshold gain of 20 points using a 5% of the equity for each trade.
Evaluates the effectiveness of a long-only strategy in this period to assess its profitability and risk-adjusted performance.
User Interface
Length: Set the length of the data used in the autocorrelation price forecasting model.
Thresold Gain: Minimum value to be considered for opening trades based on future price forecast.
Order Size: percentage size of the equity used for each single trade.
Strategy Limit
This strategy does not use a stop loss. If the price continues to drop and the future price forecast is incorrect, the trader may incur a loss or have their capital locked in the losing trade.
Disclaimer!
This is a simple template. Use the code as a starting point rather than a finished solution. The script does not include important parameters, so use it solely for educational purposes or as a boilerplate.
IU Gap Fill StrategyThe IU Gap Fill Strategy is designed to capitalize on price gaps that occur between trading sessions. It identifies gaps based on a user-defined percentage threshold and executes trades when the price fills the gap within a day. This strategy is ideal for traders looking to take advantage of market inefficiencies that arise due to overnight or session-based price movements. An ATR-based trailing stop-loss is incorporated to dynamically manage risk and lock in profits.
USER INPUTS
Percentage Difference for Valid Gap - Defines the minimum gap size in percentage terms for a valid trade setup. ( Default is 0.2 )
ATR Length - Sets the lookback period for the Average True Range (ATR) calculation. (default is 14 )
ATR Factor - Determines the multiplier for the trailing stop-loss, helping in risk management. ( Default is 2.00 )
LONG CONDITION
A gap-up occurs, meaning the current session opens above the previous session’s close.
The price initially dips below the previous session's close but then recovers and closes above it.
The gap meets the valid percentage threshold set by the user.
The bar is not the first or last bar of the session to avoid false signals.
SHORT CONDITION
A gap-down occurs, meaning the current session opens below the previous session’s close.
The price initially moves above the previous session’s close but then closes below it.
The gap meets the valid percentage threshold set by the user.
The bar is not the first or last bar of the session to avoid false signals.
LONG EXIT
An ATR-based trailing stop-loss is set below the entry price and dynamically adjusts upwards as the price moves in favor of the trade.
The position is closed when the trailing stop-loss is hit.
SHORT EXIT
An ATR-based trailing stop-loss is set above the entry price and dynamically adjusts downwards as the price moves in favor of the trade.
The position is closed when the trailing stop-loss is hit.
WHY IT IS UNIQUE
Precision in Identifying Gaps - The strategy focuses on real price gaps rather than minor fluctuations.
Dynamic Risk Management - Uses ATR-based trailing stop-loss to secure profits while allowing the trade to run.
Versatility - Works on stocks, indices, forex, and any market that experiences session-based gaps.
Optimized Entry Conditions - Ensures entries are taken only when the price attempts to fill the gap, reducing false signals.
HOW USERS CAN BENEFIT FROM IT
Enhance Trade Timing - Captures high-probability trade setups based on market inefficiencies caused by gaps.
Minimize Risk - The ATR trailing stop-loss helps protect gains and limit losses.
Works in Different Market Conditions - Whether markets are trending or consolidating, the strategy adapts to potential gap fill opportunities.
Fully Customizable - Users can fine-tune gap percentage, ATR settings, and stop-loss parameters to match their trading style.
Divergence IQ [TradingIQ]Hello Traders!
Introducing "Divergence IQ"
Divergence IQ lets traders identify divergences between price action and almost ANY TradingView technical indicator. This tool is designed to help you spot potential trend reversals and continuation patterns with a range of configurable features.
Features
Divergence Detection
Detects both regular and hidden divergences for bullish and bearish setups by comparing price movements with changes in the indicator.
Offers two detection methods: one based on classic pivot point analysis and another that provides immediate divergence signals.
Option to use closing prices for divergence detection, allowing you to choose the data that best fits your strategy.
Normalization Options:
Includes multiple normalization techniques such as robust scaling, rolling Z-score, rolling min-max, or no normalization at all.
Adjustable normalization window lets you customize the indicator to suit various market conditions.
Option to display the normalized indicator on the chart for clearer visual comparison.
Allows traders to take indicators that aren't oscillators, and convert them into an oscillator - allowing for better divergence detection.
Simulated Trade Management:
Integrates simulated trade entries and exits based on divergence signals to demonstrate potential trading outcomes.
Customizable exit strategies with options for ATR-based or percentage-based stop loss and profit target settings.
Automatically calculates key trade metrics such as profit percentage, win rate, profit factor, and total trade count.
Visual Enhancements and On-Chart Displays:
Color-coded signals differentiate between bullish, bearish, hidden bullish, and hidden bearish divergence setups.
On-chart labels, lines, and gradient flow visualizations clearly mark divergence signals, entry points, and exit levels.
Configurable settings let you choose whether to display divergence signals on the price chart or in a separate pane.
Performance Metrics Table:
A performance table dynamically displays important statistics like profit, win rate, profit factor, and number of trades.
This feature offers an at-a-glance assessment of how the divergence-based strategy is performing.
The image above shows Divergence IQ successfully identifying and trading a bullish divergence between an indicator and price action!
The image above shows Divergence IQ successfully identifying and trading a bearish divergence between an indicator and price action!
The image above shows Divergence IQ successfully identifying and trading a hidden bullish divergence between an indicator and price action!
The image above shows Divergence IQ successfully identifying and trading a hidden bearish divergence between an indicator and price action!
The performance table is designed to provide a clear summary of simulated trade results based on divergence setups. You can easily review key metrics to assess the strategy’s effectiveness over different time periods.
Customization and Adaptability
Divergence IQ offers a wide range of configurable settings to tailor the indicator to your personal trading approach. You can adjust the lookback and lookahead periods for pivot detection, select your preferred method for normalization, and modify trade exit parameters to manage risk according to your strategy. The tool’s clear visual elements and comprehensive performance metrics make it a useful addition to your technical analysis toolbox.
The image above shows Divergence IQ identifying divergences between price action and OBV with no normalization technique applied.
While traders can look for divergences between OBV and price, OBV doesn't naturally behave like an oscillator, with no definable upper and lower threshold, OBV can infinitely increase or decrease.
With Divergence IQ's ability to normalize any indicator, traders can normalize non-oscillator technical indicators such as OBV, CVD, MACD, or even a moving average.
In the image above, the "Robust Scaling" normalization technique is selected. Consequently, the output of OBV has changed and is now behaving similar to an oscillator-like technical indicator. This makes spotting divergences between the indicator and price easier and more appropriate.
The three normalization techniques included will change the indicator's final output to be more compatible with divergence detection.
This feature can be used with almost any technical indicator.
Stop Type
Traders can select between ATR based profit targets and stop losses, or percentage based profit targets and stop losses.
The image above shows options for the feature.
Divergence Detection Method
A natural pitfall of divergence trading is that it generally takes several bars to "confirm" a divergence. This makes trading the divergence complicated, because the entry at time of the divergence might look great; however, the divergence wasn't actually signaled until several bars later.
To circumvent this issue, Divergence IQ offers two divergence detection mechanisms.
Pivot Detection
Pivot detection mode is the same as almost every divergence indicator on TradingView. The Pivots High Low indicator is used to detect market/indicator highs and lows and, consequently, divergences.
This method generally finds the "best looking" divergences, but will always take additional time to confirm the divergence.
Immediate Detection
Immediate detection mode attempts to reduce lag between the divergence and its confirmation to as little as possible while avoiding repainting.
Immediate detection mode still uses the Pivots Detection model to find the first high/low of a divergence. However, the most recent high/low does not utilize the Pivot Detection model, and instead immediately looks for a divergence between price and an indicator.
Immediate Detection Mode will always signal a divergence one bar after it's occurred, and traders can set alerts in this mode to be alerted as soon as the divergence occurs.
TradingView Backtester Integration
Divergence IQ is fully compatible with the TradingView backtester!
Divergence IQ isn’t designed to be a “profitable strategy” for users to trade. Instead, the intention of including the backtester is to let users backtest divergence-based trading strategies between the asset on their chart and almost any technical indicator, and to see if divergences have any predictive utility in that market.
So while the backtester is available in Divergence IQ, it’s for users to personally figure out if they should consider a divergence an actionable insight, and not a solicitation that Divergence IQ is a profitable trading strategy. Divergence IQ should be thought of as a Divergence backtesting toolkit, not a full-feature trading strategy.
Strategy Properties Used For Backtest
Initial Capital: $1000 - a realistic amount of starting capital that will resonate with many traders
Amount Per Trade: 5% of equity - a realistic amount of capital to invest relative to portfolio size
Commission: 0.02% - a conservative amount of commission to pay for trade that is standard in crypto trading, and very high for other markets.
Slippage: 1 tick - appropriate for liquid markets, but must be increased in markets with low activity.
Once more, the backtester is meant for traders to personally figure out if divergences are actionable trading signals on the market they wish to trade with the indicator they wish to use.
And that's all!
If you have any cool features you think can benefit Divergence IQ - please feel free to share them!
Thank you so much TradingView community!
Ultimate T3 Fibonacci for BTC Scalping. Look at backtest report!Hey Everyone!
I created another script to add to my growing library of strategies and indicators that I use for automated crypto trading! This strategy is for BITCOIN on the 30 minute chart since I designed it to be a scalping strategy. I calculated for trading fees, and use a small amount of capital in the backtest report. But feel free to modify the capital and how much per order to see how it changes the results:)
It is called the "Ultimate T3 Fibonacci Indicator by NHBprod" that computes and displays two T3-based moving averages derived from price data. The t3_function calculates the Tilson T3 indicator by applying a series of exponential moving averages to a combined price metric and then blending these results with specific coefficients derived from an input factor.
The script accepts several user inputs that toggle the use of the T3 filter, select the buy signal method, and set parameters like lengths and volume factors for two variations of the T3 calculation. Two T3 lines, T3 and T32, are computed with different parameters, and their colors change dynamically (green/red for T3 and blue/purple for T32) based on whether the lines are trending upward or downward. Depending on the selected signal method, the script generates buy signals either when T32 crosses over T3 or when the closing price is above T3, and similarly, sell signals are generated on the respective conditions for crossing under or closing below. Finally, the indicator plots the T3 lines on the chart, adds visual buy/sell markers, and sets alert conditions to notify users when the respective trading signals occur.
The user has the ability to tune the parameters using TP/SL, date timerames for analyses, and the actual parameters of the T3 function including the buy/sell signal! Lastly, the user has the option of trading this long, short, or both!
Let me know your thoughts and check out the backtest report!
GOLD Volume-Based Entry StrategyShort Description:
This script identifies potential long entries by detecting two consecutive bars with above-average volume and bullish price action. When these conditions are met, a trade is entered, and an optional profit target is set based on user input. This strategy can help highlight momentum-driven breakouts or trend continuations triggered by a surge in buying volume.
How It Works
Volume Moving Average
A simple moving average of volume (vol_ma) is calculated over a user-defined period (default: 20 bars). This helps us distinguish when volume is above or below recent averages.
Consecutive Green Volume Bars
First bar: Must be bullish (close > open) and have volume above the volume MA.
Second bar: Must also be bullish, with volume above the volume MA and higher than the first bar’s volume.
When these two bars appear in sequence, we interpret it as strong buying pressure that could drive price higher.
Entry & Profit Target
Upon detecting these two consecutive bullish bars, the script places a long entry.
A profit target is set at current price plus a user-defined fixed amount (default: 5 USD).
You can adjust this target, or you can add a stop-loss in the script to manage risk further.
Visual Cues
Buy Signal Marker appears on the chart when the second bar confirms the signal.
Green Volume Columns highlight the bars that fulfill the criteria, providing a quick visual confirmation of high-volume bullish bars.
Works fine on 1M-2M-5M-15M-30M. Do not use it on higher TF. Due the lack of historical data on lower TF, the backtest result is limited.
John Bob-Trading-BotDeveloped by Ayebale John Bob with the help of his bestie, this innovative strategy combines advanced Smart Money Concepts with practical risk management tools to help traders identify and capitalize on key market moves.
Key Features:
Smart Money Concepts & Fair Value Gaps (FVG):
The strategy monitors price action for fair value gaps, which are visualized as extremely faint horizontal lines on the chart. These FVGs signal potential areas where institutional traders might have entered or exited positions.
Dynamic Entry Signals:
Buy signals are triggered when the price crosses above the 50-bar lowest low or when a bullish FVG is detected. Conversely, sell signals are generated when the price falls below the 50-bar highest high or a bearish FVG is identified. Each signal is visually marked on the chart with clear buy (green) and sell (red) labels.
Multi-Level Order Execution:
Once an entry signal occurs, the strategy places five separate orders, each with its own take-profit (TP) level. The TP levels are calculated dynamically using the Average True Range (ATR) and a set of predefined multipliers. This allows traders to scale out of positions as the market moves favorably.
Dynamic Risk Management:
A stop-loss is automatically set at a distance determined by the ATR, ensuring that risk is managed in accordance with current market volatility.
Real-Time Trade Information Table:
In the bottom-right corner of the chart, a trade information table displays essential details about the current trade:
Side: Displays "BUY NOW" (with a dark green background) for long entries or "SELL NOW" (with a dark red background) for short entries.
Entry Price & Stop-Loss: Shows the entry price (highlighted in green) and the corresponding stop-loss level (highlighted in red).
Take-Profit Levels: Lists the five TP levels, each of which turns green once the market price reaches that target.
Timer: A live timer in minutes counts from the moment the current trade trigger started, helping traders track the duration of their active trades.
Visual Progress Bar:
A histogram-style progress bar is plotted on the chart, visually representing the percentage gain (or loss) relative to the entry price.
This strategy was meticulously designed to incorporate both technical analysis and smart risk management, offering a robust trading solution that adapts to changing market conditions. Whether you're a seasoned trader or just starting out, the AyebaleJohnBob Trading Bot equips you with the tools and visual cues needed to make well-informed trading decisions. Enjoy a seamless blend of strategy and style—crafted with passion by Ayebale John Bob and his bestie!
Ultimate Stochastics Strategy by NHBprod Use to Day Trade BTCHey All!
Here's a new script I worked on that's super simple but at the same time useful. Check out the backtest results. The backtest results include slippage and fees/commission, and is still quite profitable. Obviously the profitability magnitude depends on how much capital you begin with, and how much the user utilizes per order, but in any event it seems to be profitable according to backtests.
This is different because it allows you full functionality over the stochastics calculations which is designed for random datasets. This script allows you to:
Designate ANY period of time to analyze and study
Choose between Long trading, short trading, and Long & Short trading
It allows you to enter trades based on the stochastics calculations
It allows you to EXIT trades using the stochastics calculations or take profit, or stop loss, Or any combination of those, which is nice because then the user can see how one variable effects the overall performance.
As for the actual stochastics formula, you get control, and get to SEE the plot lines for slow K, slow D, and fast K, which is usually not considered.
You also get the chance to modify the smoothing method, which has not been done with regular stochastics indicators. You get to choose the standard simple moving average (SMA) method, but I also allow you to choose other MA's such as the HMA and WMA.
Lastly, the user gets the option of using a custom trade extender, which essentially allows a buy or sell signal to exist for X amount of candles after the initial signal. For example, you can use "max bars since signal" to 1, and this will allow the indicator to produce an extra sequential buy signal when a buy signal is generated. This can be useful because it is possible that you use a small take profit (TP) and quickly exit a profitable trade. With the max bars since signal variable, you're able to reenter on the next candle and allow for another opportunity.
Let me know if you have any questions! Please take a look at the performance report and let me know your thoughts! :)
Gold Pro StrategyHere’s the strategy description in a chat format:
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**Gold (XAU/USD) Trend-Following Strategy**
This **trend-following strategy** is designed for trading gold (XAU/USD) by combining moving averages, MACD momentum indicators, and RSI filters to capture sustained trends while managing volatility risks. The strategy uses volatility-adjusted stops to protect gains and prevent overexposure during erratic price movements. The aim is to take advantage of trending markets by confirming momentum and ensuring entries are not made at extreme levels.
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**Key Components**
1. **Trend Identification**
- **50 vs 200 EMA Crossover**
- **Bullish Trend:** 50 EMA crosses above 200 EMA, and the price closes above the 200 EMA
- **Bearish Trend:** 50 EMA crosses below 200 EMA, and the price closes below the 200 EMA
2. **Momentum Confirmation**
- **MACD (12,26,9)**
- **Buy Signal:** MACD line crosses above the signal line
- **Sell Signal:** MACD line crosses below the signal line
- **RSI (14 Period)**
- **Bullish Zone:** RSI between 50-70 to avoid overbought conditions
- **Bearish Zone:** RSI between 30-50 to avoid oversold conditions
3. **Entry Criteria**
- **Long Entry:** Bullish trend, MACD bullish crossover, and RSI between 50-70
- **Short Entry:** Bearish trend, MACD bearish crossover, and RSI between 30-50
4. **Exit & Risk Management**
- **ATR Trailing Stops (14 Period):**
- Initial Stop: 3x ATR from entry price
- Trailing Stop: Adjusts to lock in profits as price moves favorably
- **Position Sizing:** 100% of equity per trade (high-risk strategy)
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**Key Logic Flow**
1. **Trend Filter:** Use the 50/200 EMA relationship to define the market's direction
2. **Momentum Confirmation:** Confirm trend momentum with MACD crossovers
3. **RSI Validation:** Ensure RSI is within non-extreme ranges before entering trades
4. **Volatility-Based Risk Management:** Use ATR stops to manage market volatility
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**Visual Cues**
- **Blue Line:** 50 EMA
- **Red Line:** 200 EMA
- **Green Triangles:** Long entry signals
- **Red Triangles:** Short entry signals
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**Strengths**
- **Clear Trend Focus:** Avoids counter-trend trades
- **RSI Filter:** Prevents entering overbought or oversold conditions
- **ATR Stops:** Adapts to gold’s inherent volatility
- **Simple Rules:** Easy to follow with minimal inputs
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**Weaknesses & Risks**
- **Infrequent Signals:** 50/200 EMA crossovers are rare
- **Potential Missed Opportunities:** Strict RSI criteria may miss some valid trends
- **Aggressive Position Sizing:** 100% equity allocation can lead to large drawdowns
- **No Profit Targets:** Relies on trailing stops rather than defined exit targets
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**Performance Profile**
| Metric | Expected Range |
|----------------------|---------------------|
| Annual Trades | 4-8 |
| Win Rate | 55-65% |
| Max Drawdown | 25-35% |
| Profit Factor | 1.8-2.5 |
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**Optimization Recommendations**
1. **Increase Trade Frequency**
Adjust the EMAs to shorter periods:
- `emaFastLen = input.int(30, "Fast EMA")`
- `emaSlowLen = input.int(150, "Slow EMA")`
2. **Relax RSI Filters**
Adjust the RSI range to:
- `rsiBullish = rsi > 45 and rsi < 75`
- `rsiBearish = rsi < 55 and rsi > 25`
3. **Add Profit Targets**
Introduce a profit target at 1.5% above entry:
```pine
strategy.exit("Long Exit", "Long",
stop=longStopPrice,
profit=close*1.015, // 1.5% target
trail_offset=trailOffset)
```
4. **Reduce Position Sizing**
Risk a smaller percentage per trade:
- `default_qty_value=25`
---
**Best Use Case**
This strategy excels in **strong trending markets** such as gold rallies during economic or geopolitical crises. However, during sideways or choppy market conditions, the strategy might require manual intervention to avoid false signals. Additionally, integrating fundamental analysis—like monitoring USD weakness or geopolitical risks—can enhance its effectiveness.
---
This strategy offers a balanced approach for trading gold, combining trend-following principles with risk management tailored to the volatility of the market.
IU Range Trading StrategyIU Range Trading Strategy
The IU Range Trading Strategy is designed to identify range-bound markets and take trades based on defined price ranges. This strategy uses a combination of price ranges and ATR (Average True Range) to filter entry conditions and incorporates a trailing stop-loss mechanism for better trade management.
User Inputs:
- Range Length: Defines the number of bars to calculate the highest and lowest price range (default: 10).
- ATR Length: Sets the length of the ATR calculation (default: 14).
- ATR Stop-Loss Factor: Determines the multiplier for the ATR-based stop-loss (default: 2.00).
Entry Conditions:
1. A range is identified when the difference between the highest and lowest prices over the selected range is less than or equal to 1.75 times the ATR.
2. Once a valid range is formed:
- A long trade is triggered at the range high.
- A short trade is triggered at the range low.
Exit Conditions:
1. Trailing Stop-Loss:
- The stop-loss adjusts dynamically using ATR targets.
- The strategy locks in profits as the trade moves in your favor.
2. The stop-loss and take-profit levels are visually plotted for transparency and easier decision-making.
Features:
- Automated box creation to visualize the trading range.
- Supports one position at a time, canceling opposite-side entries.
- ATR-based trailing stop-loss for effective risk management.
- Clear visual representation of stop-loss and take-profit levels with colored bands.
This strategy works best in markets with defined ranges and can help traders identify breakout opportunities when the price exits the range.
