LuxSignals | Trillionaire TierYou can see the Author's instructions below to get access to this indicator
Our automated trading bot is engineered for traders who demand transparency, realistic backtesting, and disciplined risk management. The system is designed not only to deliver signals but also to operate under strict trading conditions that mimic real-world markets. Every element of the strategy has been fine‐tuned so that its performance metrics—whether measured by win rate, total profit, or profit factor—this all is controlled by LuxSignals’ algorithm. This algorithm is exactly what makes it unique from all other indicators on the market: The algorithm looks at 100 possible numbers for an input and then decided on the best one - eventually you have to enter this number into the settings, leaving you with no need to adjust the settings yourself via trial and error.
Why you need to change inputs
LuxSignals is on tradingview.com, which uses the coding language "Pine Script" for its indicators. Unfortunately, Pine Script is very limited in its capabilities and can't run too many calculations at a time or can't make multiple backtests at once. Each backtest with different settings requires an indicator to load again.
An indicator will load when it's added to the chart or if its settings have changed.
This is why you have to change LuxSignals' inputs a few times so it can collect the necessary data from backtests over time to decide on its best settings. With Pine Script it's impossible to run so many backtests at once, it would require it to load longer than a minute at once, but the limit for TradingView's free tier is 20 seconds.
How LuxSignals' algorithm works
LuxSignals actually only has two inputs: Input 1 and Input 2. The minimum input for each is 2, the maximum LuxSignals can recommend is 200. You can also use higher numbers than 200 to see its backtesting results.
At the end of each process (changing the inputs many times), you will receive the best settings for the exact chart and timeframe you are on for the starting settings you had.
In total LuxSignals has to decide on one settings pair of Input 1 and Input 2 from 40'000 possible ones (200 x 200 possible combinations). It does that by calculating the hypothetical backtesting results (the profit factor) of 100 different settings at once.
You can control for which Input these hypothetical results are with the "optimize for" setting. So if you optimize for Input 1, LuxSignals calculates 100 hypothetical results for all possible inputs of Input 1, ranging from 2 to 100. When you use extension, it test the inputs from 100 to 200. The same goes for Input 2.
Now Input 1 and Input 2 affect each other's backtesting results, that's why you have to switch between optimizing for one to another.
Strategy and Signal Generation
At the core of the bot is a refined indicator that blends classic technical analysis tools with a proprietary twist. The process is as follows:
* Average Price Calculation:
The system starts by computing the average price (AP) using the formula for HLC3 (the average of the high, low, and close prices). This balanced approach captures a comprehensive view of market price action.
* Exponential Smoothing:
An exponential moving average (EMA) over a period (n₁) is applied to the AP to obtain a smoothed value (ESA). This step filters out short-term noise while maintaining sensitivity to market changes.
* Volatility Measurement:
The absolute difference between the AP and its smoothed counterpart is then subjected to another EMA (using the same period n₁) to generate a dynamic volatility metric (D). This measurement adapts to market fluctuations.
* Normalization:
The difference between the AP and ESA is normalized by dividing it by (0.015 × D). This creates a channel indicator (CI) that scales according to current volatility levels.
* WaveTrend Oscillator Formation:
A second EMA (over period n₂) is applied to the normalized value (CI) to produce the final oscillator value (WT1). The WaveTrend oscillator, therefore, encapsulates both trend strength and market volatility in a single metric.
* Signal Generation:
Trading signals are derived from the oscillator’s behavior:
* A buy signal is triggered when WT1 crosses above zero.
* A sell signal is triggered when WT1 crosses below zero.
This carefully layered approach ensures that the signals are both timely and reliable, which LuxSignals takes care of.
We decided to leave the strategy backtesting results out because we designed our own backtesting system, but we used the tradingview strategy capabilities when the trading bot is enabled in the settings.
When the bot is active, strategy mode automatically engages all realistic trading conditions. This includes using realistic account sizes, applying actual commission fees, and incorporating slippage—all calibrated to mirror live market conditions. This activation ensures that each trade is sized appropriately, with risk on any single trade limited to 5–10% of total equity, thereby preserving capital.
Please note that the following guidelines have not been fully met in our current testing process: our published backtesting results might not completely avoid potential misinterpretations by traders; we have not utilized an account size reflective of the average trader’s capital; realistic commission fees and slippage may have not been incorporated into our simulations.
In summary, while we did not apply a fully comprehensive simulation of every realistic trading condition for the backtesting, our design ensures that the strategy is equipped with realistic risk management and execution parameters when actively trading. This dual approach allows traders to benefit from both efficient signal validation and robust, live market performance without compromising on safety or accuracy.
Trading is risky & most day traders lose money. All content, tools, scripts, articles, & education provided by LuxSignals are purely for informational & educational purposes only. Past performance does not guarantee future results.
Conclusion
Our automated trading bot stands out due to its transparent and its commitment to realistic, robust strategy testing. it offers traders a dependable tool for navigating the markets. Whether you prioritize win rate, total profit, or profit factor, this system is built to adapt and perform under real-world conditions while preserving your capital.
מתנדים
Renko Buy & Sell StrategyMy ETH Contract Strategy
Hello everyone! I’ve designed a contract trading strategy specifically for ETH. The goal is to capture market volatility!
Strategy Objectives
Capture both long and short opportunities in ETH.
Use the closing price as the basis for opening and closing trades to reduce noise and avoid frequent intraday triggers.
Core Tool
Renko Chart
Core Concept
Generate buy and sell signals using the crossover of the Renko chart’s opening and closing prices, combined with dynamic adjustments based on ATR (Average True Range).
Risk Management
Position Sizing: Risk only 1 contract of the account balance per trade (adjustable).
Stop Loss
None. Close the position and open a new one when a reversal signal appears.
Take Profit
None. Close the position and open a new one when a reversal signal appears.
Why Use This Strategy?
Simple and Effective: Combines fast signals with trend filtering to avoid blind trading.
Usage Recommendations
Best suited for ETH, with optimal performance on 15-minute and 30-minute charts (also works for BTC). Not suitable for other coins. My tests started with $1,000, and the returns have been promising.
My Script//@version=6
strategy("My Script")
rsi = rsi(close, 14)
rsi_d = security(syminfo.tickerid, "D", rsi)
rsi_w = security(syminfo.tickerid, "W", rsi)
rsi_m = security(syminfo.tickerid, "M", rsi)
if crossover(rsi, 40) and rsi_d > 40 and rsi_w > 40 and rsi_m > 40
strategy.entry("buy", strategy.long)
if crossover(rsi, 60)
strategy.close("buy")
Trend-Based Major Swing Profit StrategySuited for Smart Money Controlled Markets, Particularly Central SOEs and Government-Owned Corporations in Chinese A-Shares.
Strategy Advantages
Trend Confirmation
The dual confirmation system (Yellow-Blue Ladder + Smart Money Flow) reduces false signals.
Adaptive Position Sizing
Graduated position reduction allows for capitalizing on trend continuation while managing risk.
Institutional Insight
The Smart Money component attempts to track market participation from larger, more sophisticated players.
Market Condition Awareness
Volatility and trend strength filters help avoid choppy, directionless markets.
example:300002-1d
Smart Money Following StrategyLarge-Cap Low-Volatility
example:000066-1h
Entry Conditions
A buy signal is generated when ALL of the following conditions are met:
Yellow-Blue Ladder shows an entry signal (Fast lower line > Slow lower line)
Institutional Money (MM_RSI) exceeds threshold (>5)
Market volatility is sufficient (ATR% > 3%)
Trend strength is adequate (ADX > 25)
Exit Conditions
Positions are closed when EITHER:
Yellow-Blue Ladder shows an exit signal (Fast upper line < Slow upper line)
Retail Money rises above threshold (>5), indicating excessive retail participation
$$ BTC,BNB,ETH & SOL ONLY
"Crypto Trend Strategy – Smart Entries & Top/Bottom Signals"
This strategy is designed for BTC, BNB, ETH, and SOL, using RSI, moving averages, and ATR to identify high-probability buy and short opportunities. It features:
✅ Smart Entries & Exits – Combines RSI and volume for precise entries, with ATR-based stop loss and take profit.
✅ Top & Bottom Signals – Labels potential market tops and bottoms based on RSI extremes and price action.
✅ Proven Performance – Backtested for 2024 with a 93.64% win rate and +189.83% annual profit.
Perfect for traders looking for reliable trend signals and optimized risk management. 🚀
$$ BTC,BNB,ETH & SOL ONLY
"Crypto Trend Strategy – Smart Entries & Top/Bottom Signals"
This strategy is designed for BTC, BNB, ETH, and SOL, using RSI, moving averages, and ATR to identify high-probability buy and short opportunities. It features:
✅ Smart Entries & Exits – Combines RSI and volume for precise entries, with ATR-based stop loss and take profit.
✅ Top & Bottom Signals – Labels potential market tops and bottoms based on RSI extremes and price action.
✅ Proven Performance – Backtested for 2024 with a 93.64% win rate and +189.83% annual profit.
Perfect for traders looking for reliable trend signals and optimized risk management. 🚀
Stochastic RSIStochastic RSI + EMA Trading Strategy
Overview
This strategy combines two technical indicators, Stochastic RSI and Exponential Moving Average (EMA), to make trading decisions. The core of the strategy is to trade long or short based on Stochastic RSI's overbought/oversold levels, while the EMA determines the market trend.
Indicators Used:
Stochastic RSI (StochRSI): This is an oscillator that shows when the market is overbought or oversold.
Crossing above 80: Indicates the market is overbought, and a short position is considered.
Crossing below 20: Indicates the market is oversold, and a long position is considered.
Exponential Moving Average (EMA): The EMA smooths out price data to determine the market trend.
If the price is above the EMA, the market is in an uptrend (only long positions are considered).
If the price is below the EMA, the market is in a downtrend (only short positions are considered).
Strategy Rules
1. Trend Identification (EMA):
Uptrend: If the price is above the EMA (e.g., 50-period EMA), the market is considered in an uptrend.
Downtrend: If the price is below the EMA, the market is considered in a downtrend.
2. Entry Conditions (Stochastic RSI):
Long Entry:
The price is above the EMA (uptrend).
The Stochastic RSI crosses below 20 (oversold condition), indicating a potential buying opportunity.
Short Entry:
The price is below the EMA (downtrend).
The Stochastic RSI crosses above 80 (overbought condition), indicating a potential selling opportunity.
Gold(1-10), Oil(1-3,45)fRS**Deskripsi Metode Script:**
Script ini adalah strategi trading yang dirancang untuk digunakan di platform TradingView dengan menggunakan bahasa Pine Script (versi 5). Fokus dari strategi ini adalah untuk mengidentifikasi titik masuk dan keluar berdasarkan indikator teknikal, termasuk Bollinger Bands (BB), Exponential Moving Average (EMA), Average True Range (ATR), dan fitur stop loss (SL) serta take profit (TP).
Berikut adalah penjelasan dari setiap bagian dan fungsi dalam script ini:
### 1. **Parameter Input:**
- **BB Length:** Panjang periode untuk Bollinger Bands (default: 20).
- **BB Multiplier:** Multiplier untuk deviasi standar Bollinger Bands (default: 1.9).
- **EMA Length:** Panjang periode untuk Exponential Moving Average (default: 68).
- **Use EMA Filter:** Pilihan untuk menggunakan filter EMA untuk menentukan tren pasar.
- **Use ATR for SL/TP:** Pilihan untuk menggunakan ATR dalam perhitungan level Stop Loss (SL) dan Take Profit (TP).
- **ATR Multiplier for SL:** Multiplier ATR yang digunakan untuk menghitung jarak SL (default: 2.75).
- **Stop Loss %:** Persentase dari harga saat ini yang digunakan untuk menghitung nilai Stop Loss (default: 2.75%).
- **Risk-Reward Ratio:** Rasio risiko-imbalan untuk menentukan TP (default: 1.75).
- **Use TSL:** Pilihan untuk menggunakan Trailing Stop Loss.
- **Trailing Stop ATR Multiplier:** Multiplier ATR yang digunakan untuk menentukan Trailing Stop Loss.
### 2. **Indikator Bollinger Bands (BB):**
- **Basis:** Menggunakan Simple Moving Average (SMA) dengan periode yang ditentukan oleh input `BB Length` sebagai basis Bollinger Bands.
- **Deviasi:** Deviasi standar dari harga penutupan selama periode `BB Length`, yang kemudian dikalikan dengan `BB Multiplier` untuk menentukan jarak atas dan bawah Bollinger Bands.
- **UpperBB dan LowerBB:** Menghitung level atas dan bawah dari Bollinger Bands berdasarkan basis dan deviasi.
### 3. **Filter Tren (EMA):**
- **EMA:** Exponential Moving Average dihitung berdasarkan periode `EMA Length`.
- **Trend Bullish:** Jika harga penutupan berada di atas EMA, tren dianggap bullish (naik).
- **Trend Bearish:** Jika harga penutupan berada di bawah EMA, tren dianggap bearish (turun).
### 4. **Kondisi Masuk (Entry Signal):**
- **Long Condition:** Didefinisikan sebagai crossover harga penutupan dengan Lower Bollinger Band (BB) dan tren pasar mendukung sinyal bullish, berdasarkan EMA.
- **Short Condition:** Didefinisikan sebagai crossunder harga penutupan dengan Upper Bollinger Band (BB) dan tren pasar mendukung sinyal bearish, berdasarkan EMA.
- **Alert:** Dikirimkan saat kondisi Long atau Short terpenuhi.
### 5. **Stop Loss dan Take Profit (SL/TP):**
- **ATR:** Menghitung ATR dengan periode 14 untuk mengukur volatilitas pasar.
- **SL (Stop Loss):** Dihitung berdasarkan ATR dikalikan dengan multiplier `ATR Multiplier`, atau berdasarkan persentase harga saat ini menggunakan `Stop Loss %`.
- **TP (Take Profit):** Dihitung berdasarkan rasio Risk-Reward yang ditentukan (`RiskRewardRatio`), yaitu 1.75 kali jarak Stop Loss.
- **Level SL dan TP:** Untuk posisi long, SL berada di bawah harga saat ini, dan TP berada di atas harga saat ini. Untuk posisi short, SL berada di atas harga saat ini, dan TP berada di bawah harga saat ini.
### 6. **Trailing Stop Loss (TSL):**
- **Trailing Stop:** Jika `UseTSL` diaktifkan, Trailing Stop dihitung berdasarkan ATR yang dikalikan dengan `Trailing Stop ATR Multiplier`. Trailing Stop ini bergerak seiring dengan pergerakan harga untuk melindungi keuntungan yang sudah terakumulasi.
### 7. **Alert untuk TP dan SL:**
- **Alert Condition:** Kondisi untuk memberikan alert ketika harga menembus level Take Profit (TP) atau Stop Loss (SL).
### 8. **Teks dan Label Konfirmasi di Chart:**
- **Label BUY/SELL:** Ketika kondisi long atau short terpenuhi, label akan muncul di chart yang menampilkan harga saat entry, level Stop Loss, dan level Take Profit.
- **Label SL dan TP:** Menampilkan level Stop Loss dan Take Profit yang terkait dengan posisi tersebut.
### **Kesimpulan:**
Strategi ini memanfaatkan indikator teknikal seperti Bollinger Bands untuk menentukan titik masuk dan keluar berdasarkan volatilitas pasar, serta EMA untuk menilai arah tren. Sistem ini menggunakan ATR untuk menetapkan level Stop Loss dan Take Profit yang adaptif terhadap volatilitas pasar dan menawarkan opsi Trailing Stop Loss untuk mengamankan keuntungan. Alert akan dikirimkan saat kondisi entry atau exit terjadi, membantu trader dalam pengambilan keputusan secara otomatis.
Oil FRS 30/60### Deskripsi Metode "Oil FRS 30/60"
**Metode "Oil FRS 30/60"** adalah sebuah strategi trading yang memanfaatkan **Stochastic Oscillator** pada dua timeframe yang berbeda (misalnya 30 menit dan 60 menit) untuk menghasilkan sinyal beli dan jual pada pasar minyak. Strategi ini dirancang untuk mengidentifikasi kondisi overbought (jenuh beli) atau oversold (jenuh jual) dengan memanfaatkan indikator teknikal untuk memberikan sinyal yang lebih akurat berdasarkan konfirmasi dari dua timeframe yang berbeda. Berikut adalah penjelasan lebih mendalam mengenai komponen dan cara kerja strategi ini:
### 1. **Indikator Stochastic Oscillator**
- **Stochastic Oscillator** adalah indikator momentum yang digunakan untuk menunjukkan kondisi overbought (jenuh beli) atau oversold (jenuh jual) pada harga suatu aset. Indikator ini terdiri dari dua komponen utama:
- **%K**: Menunjukkan posisi harga relatif terhadap rentang harga selama periode tertentu.
- **%D**: Merupakan rata-rata dari %K dan digunakan untuk menghaluskan sinyal.
- Dalam strategi ini, **Stochastic Oscillator** dihitung dengan periode **K Length** dan **D Length**, kemudian disaring menggunakan **Smooth K** untuk mendapatkan sinyal yang lebih halus.
### 2. **Penggunaan Dua Timeframe**
- **Timeframe Utama**: Menggunakan Stochastic Oscillator pada timeframe yang lebih rendah (misalnya, 30 menit) untuk mendapatkan sinyal trading yang lebih sensitif dan cepat.
- **Timeframe Lebih Tinggi (Higher Timeframe)**: Untuk mengonfirmasi sinyal dari timeframe utama, digunakan **Stochastic Oscillator** pada timeframe yang lebih tinggi (misalnya, 60 menit). Konfirmasi dari timeframe yang lebih tinggi membantu mengurangi kemungkinan sinyal palsu dan memastikan bahwa posisi yang diambil sesuai dengan tren yang lebih besar.
### 3. **Kondisi Sinyal Beli dan Jual**
- **Sinyal Beli (Buy Condition)**:
- **Crossover** antara %K dan %D (ketika %K melintasi %D dari bawah ke atas) pada timeframe utama.
- Nilai **%K** di bawah 20 (menunjukkan kondisi oversold).
- Pada timeframe lebih tinggi, **%K** juga harus berada di bawah 20 dan **%K** harus lebih tinggi dari **%D**, yang menunjukkan kemungkinan pembalikan arah tren.
- **Sinyal Jual (Sell Condition)**:
- **Crossunder** antara %K dan %D (ketika %K melintasi %D dari atas ke bawah) pada timeframe utama.
- Nilai **%K** di atas 80 (menunjukkan kondisi overbought).
- Pada timeframe lebih tinggi, **%K** harus berada di atas 80 dan **%K** harus lebih rendah dari **%D**, yang menunjukkan potensi pembalikan tren ke bawah.
### 4. **Manajemen Risiko: Take Profit dan Stop Loss**
- **Take Profit (TP)** dan **Stop Loss (SL)** ditentukan berdasarkan persentase dari harga saat ini.
- **Take Profit**: Level keuntungan ditentukan dengan mengalikan harga saat ini dengan faktor pengali TP (misalnya, 2.4% lebih tinggi dari harga saat ini untuk posisi beli).
- **Stop Loss**: Level kerugian ditentukan dengan mengalikan harga saat ini dengan faktor pengali SL (misalnya, 1.7% lebih rendah dari harga saat ini untuk posisi beli).
- Dengan demikian, manajemen risiko yang baik diterapkan untuk melindungi modal dan mengoptimalkan keuntungan.
### 5. **Visualisasi dan Labeling**
- **Sinyal Beli dan Jual** ditandai pada chart dengan label yang menunjukkan **BUY** atau **SELL**.
- **Level TP dan SL** digambarkan dengan garis putus-putus yang menunjukkan posisi keuntungan dan kerugian yang diinginkan.
- **Bar Color** juga diubah menjadi hijau untuk sinyal beli dan merah untuk sinyal jual untuk memudahkan visualisasi.
### 6. **Keunggulan Metode Ini**
- **Konfirmasi dari Dua Timeframe**: Penggunaan dua timeframe membantu memastikan bahwa sinyal yang dihasilkan lebih valid, mengurangi kemungkinan sinyal palsu dan meningkatkan keakuratan dalam pengambilan keputusan.
- **Strategi Momentum**: Dengan menggunakan indikator Stochastic, strategi ini mengandalkan identifikasi kondisi jenuh beli atau jenuh jual, yang sering kali menunjukkan potensi pembalikan arah harga.
- **Manajemen Risiko yang Jelas**: Dengan adanya level TP dan SL yang dinamis, trader dapat dengan mudah mengontrol risiko dan mengatur target keuntungan.
### 7. **Cocok Untuk Apa?**
- **Minyak (Oil)**: Metode ini dirancang khusus untuk pasar minyak, karena harga minyak cenderung mengalami fluktuasi yang cepat dan seringkali terpengaruh oleh berita ekonomi dan politik. Oleh karena itu, penggunaan dua timeframe untuk konfirmasi sinyal menjadi sangat penting.
- **Trader Jangka Pendek hingga Menengah**: Strategi ini cocok untuk trader yang lebih suka melakukan trading dalam jangka pendek hingga menengah (scalping atau swing trading), dengan memanfaatkan pergerakan harga yang cepat dalam timeframe rendah dan tinggi.
### Kesimpulan
Strategi **Oil FRS 30/60** mengombinasikan indikator teknikal Stochastic Oscillator pada dua timeframe berbeda untuk memberikan sinyal beli dan jual yang lebih akurat. Dengan konfirmasi dari timeframe lebih tinggi dan pengaturan manajemen risiko yang baik melalui Take Profit dan Stop Loss, strategi ini dirancang untuk meminimalkan risiko dan memaksimalkan potensi keuntungan dalam trading minyak.
TEMA OBOS Strategy PakunTEMA OBOS Strategy
Overview
This strategy combines a trend-following approach using the Triple Exponential Moving Average (TEMA) with Overbought/Oversold (OBOS) indicator filtering.
By utilizing TEMA crossovers to determine trend direction and OBOS as a filter, it aims to improve entry precision.
This strategy can be applied to markets such as Forex, Stocks, and Crypto, and is particularly designed for mid-term timeframes (5-minute to 1-hour charts).
Strategy Objectives
Identify trend direction using TEMA
Use OBOS to filter out overbought/oversold conditions
Implement ATR-based dynamic risk management
Key Features
1. Trend Analysis Using TEMA
Uses crossover of short-term EMA (ema3) and long-term EMA (ema4) to determine entries.
ema4 acts as the primary trend filter.
2. Overbought/Oversold (OBOS) Filtering
Long Entry Condition: up > down (bullish trend confirmed)
Short Entry Condition: up < down (bearish trend confirmed)
Reduces unnecessary trades by filtering extreme market conditions.
3. ATR-Based Take Profit (TP) & Stop Loss (SL)
Adjustable ATR multiplier for TP/SL
Default settings:
TP = ATR × 5
SL = ATR × 2
Fully customizable risk parameters.
4. Customizable Parameters
TEMA Length (for trend calculation)
OBOS Length (for overbought/oversold detection)
Take Profit Multiplier
Stop Loss Multiplier
EMA Display (Enable/Disable TEMA lines)
Bar Color Change (Enable/Disable candle coloring)
Trading Rules
Long Entry (Buy Entry)
ema3 crosses above ema4 (Golden Cross)
OBOS indicator confirms up > down (bullish trend)
Execute a buy position
Short Entry (Sell Entry)
ema3 crosses below ema4 (Death Cross)
OBOS indicator confirms up < down (bearish trend)
Execute a sell position
Take Profit (TP)
Entry Price + (ATR × TP Multiplier) (Default: 5)
Stop Loss (SL)
Entry Price - (ATR × SL Multiplier) (Default: 2)
TP/SL settings are fully customizable to fine-tune risk management.
Risk Management Parameters
This strategy emphasizes proper position sizing and risk control to balance risk and return.
Trading Parameters & Considerations
Initial Account Balance: $7,000 (adjustable)
Base Currency: USD
Order Size: 10,000 USD
Pyramiding: 1
Trading Fees: $0.94 per trade
Long Position Margin: 50%
Short Position Margin: 50%
Total Trades (M5 Timeframe): 128
Deep Test Results (2024/11/01 - 2025/02/24)BTCUSD-5M
Total P&L：+1638.20USD
Max equity drawdown：694.78USD
Total trades：128
Profitable trades:44.53
Profit factor:1.45
These settings aim to protect capital while maintaining a balanced risk-reward approach.
Visual Support
TEMA Lines (Three EMAs)
Trend direction is indicated by color changes (Blue/Orange)
ema3 (short-term) and ema4 (long-term) crossover signals potential entries
OBOS Histogram
Green → Strong buying pressure
Red → Strong selling pressure
Blue → Possible trend reversal
Entry & Exit Markers
Blue Arrow → Long Entry Signal
Red Arrow → Short Entry Signal
Take Profit / Stop Loss levels displayed
Strategy Improvements & Uniqueness
This strategy is based on indicators developed by "l_lonthoff" and "jdmonto0", but has been significantly optimized for better entry accuracy, visual clarity, and risk management.
Enhanced Trend Identification with TEMA
Detects early trend reversals using ema3 & ema4 crossover
Reduces market noise for a smoother trend-following approach
Improved OBOS Filtering
Prevents excessive trading
Reduces unnecessary risk exposure
Dynamic Risk Management with ATR-Based TP/SL
Not a fixed value → TP/SL adjusts to market volatility
Fully customizable ATR multiplier settings
(Default: TP = ATR × 5, SL = ATR × 2)
Summary
The TEMA + OBOS Strategy is a simple yet powerful trading method that integrates trend analysis and oscillators.
TEMA for trend identification
OBOS for noise reduction & overbought/oversold filtering
ATR-based TP/SL settings for dynamic risk management
Before using this strategy, ensure thorough backtesting and demo trading to fine-tune parameters according to your trading style.
Strategy SuperTrend SDI WebhookThis Pine Script™ strategy is designed for automated trading in TradingView. It combines the SuperTrend indicator and Smoothed Directional Indicator (SDI) to generate buy and sell signals, with additional risk management features like stop loss, take profit, and trailing stop. The script also includes settings for leverage trading, equity-based position sizing, and webhook integration.
Key Features
1. Date-based Trade Execution
The strategy is active only between the start and end dates set by the user.
times ensures that trades occur only within this predefined time range.
2. Position Sizing and Leverage
Uses leverage trading to adjust position size dynamically based on initial equity.
The user can set leverage (leverage) and percentage of equity (usdprcnt).
The position size is calculated dynamically (initial_capital) based on account performance.
3. Take Profit, Stop Loss, and Trailing Stop
Take Profit (tp): Defines the target profit percentage.
Stop Loss (sl): Defines the maximum allowable loss per trade.
Trailing Stop (tr): Adjusts dynamically based on trade performance to lock in profits.
4. SuperTrend Indicator
SuperTrend (ta.supertrend) is used to determine the market trend.
If the price is above the SuperTrend line, it indicates an uptrend (bullish).
If the price is below the SuperTrend line, it signals a downtrend (bearish).
Plots visual indicators (green/red lines and circles) to show trend changes.
5. Smoothed Directional Indicator (SDI)
SDI helps to identify trend strength and momentum.
It calculates +DI (bullish strength) and -DI (bearish strength).
If +DI is higher than -DI, the market is considered bullish.
If -DI is higher than +DI, the market is considered bearish.
The background color changes based on the SDI signal.
6. Buy & Sell Conditions
Long Entry (Buy) Conditions:
SDI confirms an uptrend (+DI > -DI).
SuperTrend confirms an uptrend (price crosses above the SuperTrend line).
Short Entry (Sell) Conditions:
SDI confirms a downtrend (+DI < -DI).
SuperTrend confirms a downtrend (price crosses below the SuperTrend line).
Optionally, trades can be filtered using crossovers (occrs option).
7. Trade Execution and Exits
Market entries:
Long (strategy.entry("Long")) when conditions match.
Short (strategy.entry("Short")) when bearish conditions are met.
Trade exits:
Uses predefined take profit, stop loss, and trailing stop levels.
Positions are closed if the strategy is out of the valid time range.
Usage
Automated Trading Strategy:
Can be integrated with webhooks for automated execution on supported trading platforms.
Trend-Following Strategy:
Uses SuperTrend & SDI to identify trend direction and strength.
Risk-Managed Leverage Trading:
Supports position sizing, stop losses, and trailing stops.
Backtesting & Optimization:
Can be used for historical performance analysis before deploying live.
Conclusion
This strategy is suitable for traders who want to automate their trading using SuperTrend and SDI indicators. It incorporates risk management tools like stop loss, take profit, and trailing stop, making it adaptable for leverage trading. Traders can customize settings, conduct backtests, and integrate it with webhooks for real-time trade execution. 🚀
Important Note:
This script is provided for educational and template purposes and does not constitute financial advice. Traders and investors should conduct their research and analysis before making any trading decisions.
Enhanced BarUpDn StrategyEnhanced BarUpDn Strategy
The Enhanced BarUpDn Strategy is a refined price action-based trading approach that identifies market trends and reversals using bar formations. It focuses on detecting bullish and bearish momentum by analyzing consecutive price bars and key support/resistance levels.
Key Features:
✅ Trend Confirmation – Uses a combination of bar patterns and indicators (e.g., moving averages, RSI) to confirm momentum shifts.
✅ Entry Signals – A buy signal is triggered when an "Up Bar" (higher high, higher low) follows a bullish setup; a sell signal when a "Down Bar" (lower high, lower low) confirms bearish momentum.
✅ Enhanced Filters – Incorporates volume analysis and additional conditions to reduce false signals.
✅ Stop-Loss & Risk Management – Uses recent swing highs/lows for stop placement and dynamic trailing stops for maximizing gains.
ADX for BTC [PineIndicators]The ADX Strategy for BTC is a trend-following system that uses the Average Directional Index (ADX) to determine market strength and momentum shifts. Designed for Bitcoin trading, this strategy applies a customizable ADX threshold to confirm trend signals and optionally filters entries using a Simple Moving Average (SMA). The system features automated entry and exit conditions, dynamic trade visualization, and built-in trade tracking for historical performance analysis.
⚙️ Core Strategy Components
1️⃣ Average Directional Index (ADX) Calculation
The ADX indicator measures trend strength without indicating direction. It is derived from the Positive Directional Movement (+DI) and Negative Directional Movement (-DI):
+DI (Positive Directional Index): Measures upward price movement.
-DI (Negative Directional Index): Measures downward price movement.
ADX Value: Higher values indicate stronger trends, regardless of direction.
This strategy uses a default ADX length of 14 to smooth out short-term fluctuations while detecting sustainable trends.
2️⃣ SMA Filter (Optional Trend Confirmation)
The strategy includes a 200-period SMA filter to validate trend direction before entering trades. If enabled:
✅ Long Entry is only allowed when price is above a long-term SMA multiplier (5x the standard SMA length).
✅ If disabled, the strategy only considers the ADX crossover threshold for trade entries.
This filter helps reduce entries in sideways or weak-trend conditions, improving signal reliability.
📌 Trade Logic & Conditions
🔹 Long Entry Conditions
A buy signal is triggered when:
✅ ADX crosses above the threshold (default = 14), indicating a strengthening trend.
✅ (If SMA filter is enabled) Price is above the long-term SMA multiplier.
🔻 Exit Conditions
A position is closed when:
✅ ADX crosses below the stop threshold (default = 45), signaling trend weakening.
By adjusting the entry and exit ADX levels, traders can fine-tune sensitivity to trend changes.
📏 Trade Visualization & Tracking
Trade Markers
"Buy" label (▲) appears when a long position is opened.
"Close" label (▼) appears when a position is exited.
Trade History Boxes
Green if a trade is profitable.
Red if a trade closes at a loss.
Trend Tracking Lines
Horizontal lines mark entry and exit prices.
A filled trade box visually represents trade duration and profitability.
These elements provide clear visual insights into trade execution and performance.
⚡ How to Use This Strategy
1️⃣ Apply the script to a BTC chart in TradingView.
2️⃣ Adjust ADX entry/exit levels based on trend sensitivity.
3️⃣ Enable or disable the SMA filter for trend confirmation.
4️⃣ Backtest performance to analyze historical trade execution.
5️⃣ Monitor trade markers and history boxes for real-time trend insights.
This strategy is designed for trend traders looking to capture high-momentum market conditions while filtering out weak trends.
Balance of Power for US30 4H [PineIndicators]The Balance of Power (BoP) Strategy is a momentum-based trading system for the US30 index on a 4-hour timeframe. It measures the strength of buyers versus sellers in each candle using the Balance of Power (BoP) indicator and executes trades based on predefined threshold crossovers. The strategy includes dynamic position sizing, adjustable leverage, and visual trade tracking.
⚙️ Core Strategy Mechanics
Positive values indicate buying strength.
Negative values indicate selling strength.
Values close to 1 suggest strong bullish momentum.
Values close to -1 indicate strong bearish pressure.
The strategy uses fixed threshold crossovers to determine trade entries and exits.
📌 Trade Logic
Entry Conditions
Long Entry: When BoP crosses above 0.8, signaling strong buying pressure.
Exit Conditions
Position Close: When BoP crosses below -0.8, indicating a shift to selling pressure.
This threshold-based system filters out low-confidence signals and focuses on high-momentum shifts.
📏 Position Sizing & Leverage
Leverage: Adjustable by the user (default = 5x).
Risk Management: Position size adapts dynamically based on equity fluctuations.
📊 Trade Visualization & History Tracking
Trade Markers:
"Buy" labels appear when a long position is opened.
"Close" labels appear when a position is exited.
Trade History Boxes:
Green for profitable trades.
Red for losing trades.
These elements provide clear visual tracking of past trade execution.
⚡ Usage & Customization
1️⃣ Apply the script to a US30 4H chart in TradingView.
2️⃣ Adjust leverage settings as needed.
3️⃣ Review trade signals and historical performance with visual markers.
4️⃣ Enable backtesting to evaluate past performance.
This strategy is designed for momentum-based trading and is best suited for volatile market conditions.
TSI Long/Short for BTC 2HThe TSI Long/Short for BTC 2H strategy is an advanced trend-following system designed specifically for trading Bitcoin (BTC) on a 2-hour timeframe. It leverages the True Strength Index (TSI) to identify momentum shifts and executes both long and short trades in response to dynamic market conditions.
Unlike traditional moving average-based strategies, this script uses a double-smoothed momentum calculation, enhancing signal accuracy and reducing noise. It incorporates automated position sizing, customizable leverage, and real-time performance tracking, ensuring a structured and adaptable trading approach.
🔹 What Makes This Strategy Unique?
Unlike simple crossover strategies or generic trend-following approaches, this system utilizes a customized True Strength Index (TSI) methodology that dynamically adjusts to market conditions.
🔸 True Strength Index (TSI) Filtering – The script refines the TSI by applying double exponential smoothing, filtering out weak signals and capturing high-confidence momentum shifts.
🔸 Adaptive Entry & Exit Logic – Instead of fixed thresholds, it compares the TSI value against a dynamically determined high/low range from the past 100 bars to confirm trade signals.
🔸 Leverage & Risk Optimization – Position sizing is dynamically adjusted based on account equity and leverage settings, ensuring controlled risk exposure.
🔸 Performance Monitoring System – A built-in performance tracking table allows traders to evaluate monthly and yearly results directly on the chart.
📊 Core Strategy Components
1️⃣ Momentum-Based Trade Execution
The strategy generates long and short trade signals based on the following conditions:
✅ Long Entry Condition – A buy signal is triggered when the TSI crosses above its 100-bar highest value (previously set), confirming bullish momentum.
✅ Short Entry Condition – A sell signal is generated when the TSI crosses below its 100-bar lowest value (previously set), indicating bearish pressure.
Each trade execution is fully automated, reducing emotional decision-making and improving trading discipline.
2️⃣ Position Sizing & Leverage Control
Risk management is a key focus of this strategy:
🔹 Dynamic Position Sizing – The script calculates position size based on:
Account Equity – Ensuring trade sizes adjust dynamically with capital fluctuations.
Leverage Multiplier – Allows traders to customize risk exposure via an adjustable leverage setting.
🔹 No Fixed Stop-Loss – The strategy relies on reversals to exit trades, meaning each position is closed when the opposite signal appears.
This design ensures maximum capital efficiency while adapting to market conditions in real time.
3️⃣ Performance Visualization & Tracking
Understanding historical performance is crucial for refining strategies. The script includes:
📌 Real-Time Trade Markers – Buy and sell signals are visually displayed on the chart for easy reference.
📌 Performance Metrics Table – Tracks monthly and yearly returns in percentage form, helping traders assess profitability over time.
📌 Trade History Visualization – Completed trades are displayed with color-coded boxes (green for long trades, red for short trades), visually representing profit/loss dynamics.
📢 Why Use This Strategy?
✔ Advanced Momentum Detection – Uses a double-smoothed TSI for more accurate trend signals.
✔ Fully Automated Trading – Removes emotional bias and enforces discipline.
✔ Customizable Risk Management – Adjust leverage and position sizing to suit your risk profile.
✔ Comprehensive Performance Tracking – Integrated reporting system provides clear insights into past trades.
This strategy is ideal for Bitcoin traders looking for a structured, high-probability system that adapts to both bullish and bearish trends on the 2-hour timeframe.
📌 How to Use: Simply add the script to your 2H BTC chart, configure your leverage settings, and let the system handle trade execution and tracking! 🚀
[SHORT ONLY] Internal Bar Strength (IBS) Mean Reversion Strategy█ STRATEGY DESCRIPTION
The "Internal Bar Strength (IBS) Strategy" is a mean-reversion strategy designed to identify trading opportunities based on the closing price's position within the daily price range. It enters a short position when the IBS indicates overbought conditions and exits when the IBS reaches oversold levels. This strategy is Short-Only and was designed to be used on the Daily timeframe for Stocks and ETFs.
█ WHAT IS INTERNAL BAR STRENGTH (IBS)?
Internal Bar Strength (IBS) measures where the closing price falls within the high-low range of a bar. It is calculated as:
IBS = (Close - Low) / (High - Low)
- Low IBS (≤ 0.2) : Indicates the close is near the bar's low, suggesting oversold conditions.
- High IBS (≥ 0.8) : Indicates the close is near the bar's high, suggesting overbought conditions.
█ SIGNAL GENERATION
1. SHORT ENTRY
A Short Signal is triggered when:
The IBS value rises to or above the Upper Threshold (default: 0.9).
The Closing price is greater than the previous bars High (close>high ).
The signal occurs within the specified time window (between `Start Time` and `End Time`).
2. EXIT CONDITION
An exit Signal is generated when the IBS value drops to or below the Lower Threshold (default: 0.3). This prompts the strategy to exit the position.
█ ADDITIONAL SETTINGS
Upper Threshold: The IBS level at which the strategy enters trades. Default is 0.9.
Lower Threshold: The IBS level at which the strategy exits short positions. Default is 0.3.
Start Time and End Time: The time window during which the strategy is allowed to execute trades.
█ PERFORMANCE OVERVIEW
This strategy is designed for Stocks and ETFs markets and performs best when prices frequently revert to the mean.
The strategy can be optimized further using additional conditions such as using volume or volatility filters.
It is sensitive to extreme IBS values, which help identify potential reversals.
Backtesting results should be analyzed to optimize the Upper/Lower Thresholds for specific instruments and market conditions.
Iron Bot Statistical Trend Filter📌 Iron Bot Statistical Trend Filter
📌 Overview
Iron Bot Statistical Trend Filter is an advanced trend filtering strategy that combines statistical methods with technical analysis.
By leveraging Z-score and Fibonacci levels, this strategy quantitatively analyzes market trends to provide high-precision entry signals.
Additionally, it includes an optional EMA filter to enhance trend reliability.
Risk management is reinforced with Stop Loss (SL) and four Take Profit (TP) levels, ensuring a balanced approach to risk and reward.
📌 Key Features
🔹 1. Statistical Trend Filtering with Z-Score
This strategy calculates the Z-score to measure how much the price deviates from its historical mean.
Positive Z-score: Indicates a statistically high price, suggesting a strong uptrend.
Negative Z-score: Indicates a statistically low price, signaling a potential downtrend.
Z-score near zero: Suggests a ranging market with no strong trend.
By using the Z-score as a filter, market noise is reduced, leading to more reliable entry signals.
🔹 2. Fibonacci Levels for Trend Reversal Detection
The strategy integrates Fibonacci retracement levels to identify potential reversal points in the market.
High Trend Level (Fibo 23.6%): When the price surpasses this level, an uptrend is likely.
Low Trend Level (Fibo 78.6%): When the price falls below this level, a downtrend is expected.
Trend Line (Fibo 50%): Acts as a midpoint, helping to assess market balance.
This allows traders to visually confirm trend strength and turning points, improving entry accuracy.
🔹 3. EMA Filter for Trend Confirmation (Optional)
The strategy includes an optional 200 EMA (Exponential Moving Average) filter for trend validation.
Price above 200 EMA: Indicates a bullish trend (long entries preferred).
Price below 200 EMA: Indicates a bearish trend (short entries preferred).
Enabling this filter reduces false signals and improves trend-following accuracy.
🔹 4. Multi-Level Take Profit (TP) and Stop Loss (SL) Management
To ensure effective risk management, the strategy includes four Take Profit levels and a Stop Loss:
Stop Loss (SL): Automatically closes trades when the price moves against the position by a certain percentage.
TP1 (+0.75%): First profit-taking level.
TP2 (+1.1%): A higher probability profit target.
TP3 (+1.5%): Aiming for a stronger trend move.
TP4 (+2.0%): Maximum profit target.
This system secures profits at different stages and optimizes risk-reward balance.
🔹 5. Automated Long & Short Trading Logic
The strategy is built using Pine Script®’s strategy.entry() and strategy.exit(), allowing fully automated trading.
Long Entry:
Price is above the trend line & high trend level.
Z-score is positive (indicating an uptrend).
(Optional) Price is also above the EMA for stronger confirmation.
Short Entry:
Price is below the trend line & low trend level.
Z-score is negative (indicating a downtrend).
(Optional) Price is also below the EMA for stronger confirmation.
This logic helps filter out unnecessary trades and focus only on high-probability entries.
📌 Trading Parameters
This strategy is designed for flexible capital management and risk control.
💰 Account Size: $5000
📉 Commissions and Slippage: Assumes 94 pips commission per trade and 1 pip slippage.
⚖️ Risk per Trade: Adjustable, with a default setting of 1% of equity.
These parameters help preserve capital while optimizing the risk-reward balance.
📌 Visual Aids for Clarity
To enhance usability, the strategy includes clear visual elements for easy market analysis.
✅ Trend Line (Blue): Indicates market midpoint and helps with entry decisions.
✅ Fibonacci Levels (Yellow): Highlights high and low trend levels.
✅ EMA Line (Green, Optional): Confirms long-term trend direction.
✅ Entry Signals (Green for Long, Red for Short): Clearly marked buy and sell signals.
These features allow traders to quickly interpret market conditions, even without advanced technical analysis skills.
📌 Originality & Enhancements
This strategy is developed based on the IronXtreme and BigBeluga indicators,
combining a unique Z-score statistical method with Fibonacci trend analysis.
Compared to conventional trend-following strategies, it leverages statistical techniques
to provide higher-precision entry signals, reducing false trades and improving overall reliability.
📌 Summary
Iron Bot Statistical Trend Filter is a statistically-driven trend strategy that utilizes Z-score and Fibonacci levels.
High-precision trend analysis
Enhanced accuracy with an optional EMA filter
Optimized risk management with multiple TP & SL levels
Visually intuitive chart design
Fully customizable parameters & leverage support
This strategy reduces false signals and helps traders ride the trend with confidence.
Try it out and take your trading to the next level! 🚀
Volatility Arbitrage Spread Oscillator Model (VASOM)The Volatility Arbitrage Spread Oscillator Model (VASOM) is a systematic approach to capitalizing on price inefficiencies in the VIX futures term structure. By analyzing the differential between front-month and second-month VIX futures contracts, we employ a momentum-based oscillator (Relative Strength Index, RSI) to signal potential market reversion opportunities. Our research builds upon existing financial literature on volatility risk premia and contango/backwardation dynamics in the volatility markets (Zhang & Zhu, 2006; Alexander & Korovilas, 2012).
Volatility derivatives have become essential tools for managing risk and engaging in speculative trades (Whaley, 2009). The Chicago Board Options Exchange (CBOE) Volatility Index (VIX) measures the market’s expectation of 30-day forward-looking volatility derived from S&P 500 option prices (CBOE, 2018). Term structures in VIX futures often exhibit contango or backwardation, depending on macroeconomic and market conditions (Alexander & Korovilas, 2012).
This strategy seeks to exploit the spread between the front-month and second-month VIX futures as a proxy for term structure dynamics. The spread’s momentum, quantified by the RSI, serves as a signal for entry and exit points, aligning with empirical findings on mean reversion in volatility markets (Zhang & Zhu, 2006).
• Entry Signal: When RSI_t falls below the user-defined threshold (e.g., 30), indicating a potential undervaluation in the spread.
• Exit Signal: When RSI_t exceeds a threshold (e.g., 70), suggesting mean reversion has occurred.
Empirical Justification
The strategy aligns with findings that suggest predictable patterns in volatility futures spreads (Alexander & Korovilas, 2012). Furthermore, the use of RSI leverages insights from momentum-based trading models, which have demonstrated efficacy in various asset classes, including commodities and derivatives (Jegadeesh & Titman, 1993).
References
• Alexander, C., & Korovilas, D. (2012). The Hazards of Volatility Investing. Journal of Alternative Investments, 15(2), 92-104.
• CBOE. (2018). The VIX White Paper. Chicago Board Options Exchange.
• Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91.
• Zhang, C., & Zhu, Y. (2006). Exploiting Predictability in Volatility Futures Spreads. Financial Analysts Journal, 62(6), 62-72.
• Whaley, R. E. (2009). Understanding the VIX. The Journal of Portfolio Management, 35(3), 98-105.
Macro-Sentiment Index Model (MSIM)Macro-Sentiment Index Model (MSIM) is a comprehensive trading strategy developed to analyze and interpret the broader macroeconomic and market sentiment. The strategy integrates various quantitative signals, including market volatility, trading volume, market breadth, and economic indicators, to assess the prevailing mood in the financial markets. This sentiment analysis is then used to guide trading decisions, helping identify optimal entry and exit points based on underlying market conditions. The model is specifically designed to capture the shifts in investor sentiment, which have been shown to significantly influence market behavior (Fleming et al., 2001).
The MSIM utilizes a multi-faceted approach to measure sentiment. Drawing from the theory that macroeconomic variables can influence financial markets (Stock & Watson, 2002), the strategy incorporates market volatility (VIX), volume measures, and long-term market trends. These indicators help form a robust view of the market’s risk appetite and potential for price movement. For instance, high volatility often signals increased market uncertainty (Bollerslev, 1986), while volume-based indicators provide insights into investor conviction (Chen, 1991).
Additionally, the model incorporates macroeconomic proxies like GDP growth, interest rates, and unemployment data, leveraging the findings of macroeconomic studies that indicate a direct correlation between these factors and market performance (Hamilton, 1994). By normalizing these economic indicators, the model provides a standardized sentiment score that reflects the aggregated impact of these factors on the market’s outlook.
The MSIM aims to exploit market inefficiencies by responding to shifts in sentiment before they manifest in price movements. Studies have shown that sentiment indicators, such as the Advance-Decline Line and the Stock-Bond Ratio, can be predictive of future price movements (Neely, 2010). The model integrates these indicators into a single composite sentiment score, which is then filtered through momentum signals to refine entry points. This approach is grounded in behavioral finance theory, which suggests that investor sentiment plays a crucial role in driving asset prices, sometimes beyond the reach of fundamental data alone (Shiller, 2000).
The strategy is designed to identify long opportunities when sentiment is particularly favorable, with a focus on minimizing risk during adverse conditions. By analyzing market trends alongside macroeconomic signals, the MSIM helps traders stay aligned with the prevailing market forces.
References:
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
• Chen, S. S. (1991). The determinants of stock market liquidity. Journal of Financial and Quantitative Analysis, 26(3), 283-305.
• Fleming, M. J., Kirby, C. W., & Ostdiek, B. (2001). The economic value of volatility timing. Journal of Financial and Quantitative Analysis, 36(1), 113-134.
• Hamilton, J. D. (1994). Time series analysis. Princeton University Press.
• Neely, C. J. (2010). The behavior of exchange rates: A survey of recent empirical literature. International Finance Discussion Papers, 981.
• Shiller, R. J. (2000). Irrational Exuberance. Princeton University Press.
• Stock, J. H., & Watson, M. W. (2002). Macroeconomic forecasting using diffusion indexes. Journal of Business & Economic Statistics, 20(2), 147-162.
Statistical Arbitrage Pairs Trading - Long-Side OnlyThis strategy implements a simplified statistical arbitrage (" stat arb ") approach focused on mean reversion between two correlated instruments. It identifies opportunities where the spread between their normalized price series (Z-scores) deviates significantly from historical norms, then executes long-only trades anticipating reversion to the mean.
Key Mechanics:
1. Spread Calculation: The strategy computes Z-scores for both instruments to normalize price movements, then tracks the spread between these Z-scores.
2. Modified Z-Score: Uses a robust measure combining the median and Median Absolute Deviation (MAD) to reduce outlier sensitivity.
3. Entry Signal: A long position is triggered when the spread’s modified Z-score falls below a user-defined threshold (e.g., -1.0), indicating extreme undervaluation of the main instrument relative to its pair.
4. Exit Signal: The position closes automatically when the spread reverts to its historical mean (Z-score ≥ 0).
Risk management:
Trades are sized as a percentage of equity (default: 10%).
Includes commissions and slippage for realistic backtesting.
Tutorial - Adding sessions to strategiesA simple script to illustrate how to add sessions to trading strategies.
In this interactive tutorial, you'll learn how to add trading sessions to your strategies using Pine Script. By the end of this session (pun intended!), you'll be able to create custom trading windows that adapt to changing market conditions.
What You'll Learn:
Defining Trading Sessions: Understand how to set up specific time frames for buying and selling, tailored to your unique trading style.
RSI-Based Entry Signals: Discover how to use the Relative Strength Index (RSI) as a trigger for buy and sell signals, helping you capitalize on market trends.
Combining Session Logic with Trading Decisions: Learn how to integrate session-based logic into your strategy, ensuring that trades are executed only during designated times.
By combining these elements, we create an interactive strategy that:
1. Generates buy and sell signals based on RSI levels.
2. Checks if the market is open during a specific trading session (e.g., 1300-1700).
3. Executes trades only when both conditions are met.
**Tips & Variations:**
* Experiment with different RSI periods, thresholds, and sessions to optimize your strategy for various markets and time frames.
* Consider adding more advanced logic, such as stop-losses or position sizing, to further refine your trading approach.
Get ready to take your Pine Script skills to the next level!
~Description partially generated with Llama3_8B
Internal Bar Strength (IBS) Strategy█ STRATEGY DESCRIPTION
The "Internal Bar Strength (IBS) Strategy" is a mean-reversion strategy designed to identify trading opportunities based on the closing price's position within the daily price range. It enters a long position when the IBS indicates oversold conditions and exits when the IBS reaches overbought levels. This strategy was designed to be used on the daily timeframe.
█ WHAT IS INTERNAL BAR STRENGTH (IBS)?
Internal Bar Strength (IBS) measures where the closing price falls within the high-low range of a bar. It is calculated as:
IBS = (Close - Low) / (High - Low)
- **Low IBS (≤ 0.2)**: Indicates the close is near the bar's low, suggesting oversold conditions.
- **High IBS (≥ 0.8)**: Indicates the close is near the bar's high, suggesting overbought conditions.
█ SIGNAL GENERATION
1. LONG ENTRY
A Buy Signal is triggered when:
The IBS value drops below the Lower Threshold (default: 0.2).
The signal occurs within the specified time window (between `Start Time` and `End Time`).
2. EXIT CONDITION
A Sell Signal is generated when the IBS value rises to or above the Upper Threshold (default: 0.8). This prompts the strategy to exit the position.
█ ADDITIONAL SETTINGS
Upper Threshold: The IBS level at which the strategy exits trades. Default is 0.8.
Lower Threshold: The IBS level at which the strategy enters long positions. Default is 0.2.
Start Time and End Time: The time window during which the strategy is allowed to execute trades.
█ PERFORMANCE OVERVIEW
This strategy is designed for ranging markets and performs best when prices frequently revert to the mean.
It is sensitive to extreme IBS values, which help identify potential reversals.
Backtesting results should be analyzed to optimize the Upper/Lower Thresholds for specific instruments and market conditions.
