SOL Smart Alert SystemITECS built this to work alongside my AI agent and scripts to provide a robust notification/alert system that can be configured to best work with the current market conditions.
Statistics
Eliora Gold 1min (Heikin Ashi)Eliora -focused trading strategy designed for anything on the 1-minute timeframe using Heikin Ashi candles. This mode combines advanced market logic with structured risk management to deliver smooth, disciplined trade execution.
Key Features:
✅ Trend Confirmation – Aligns with dominant market direction for higher accuracy.
✅ ATR-Based Volatility Filter – Avoids high-risk conditions and chaotic price action.
✅ Candle Strength Logic – Filters weak setups, focusing on strong momentum.
✅ Balanced Risk/Reward – Calculates stop-loss and take-profit dynamically for consistent results.
✅ Cooldown & Overtrade Protection – Limits frequency to maintain trade quality.
This version of Eliora is built for scalpers and intraday traders seeking high-probability entries with graceful exits.
PCR tableOverview
This indicator displays a multi-period table of forward-looking price projections. It combines normalized directional momentum (Positive Change Ratio, PCR) with volatility (ATR) and presents a forecast for upcoming time intervals, adjusted for your local UTC offset.
Concepts & Calculations
Positive Change Ratio (PCR):
((total positive change)/(total change)-0.5)*2, producing a value between –100 and +100.
Synthetic ATR: Calculates average true range over the same lookbacks to capture volatility.
PCR × ATR: Forms a volatility-weighted directional forecast, indicating expected move magnitude.
Future Price Projection: Adds PCR × ATR value to current close to estimate future price at each lookahead interval.
Table Layout
There are 12 forecast horizons—1× to 12× the chart timeframe (e.g., minutes, hours, days). Each row displays:
1. Future Time: Timestamp of each projection (adjustable via UTC offset)
2. PCR: Directional bias per period (–1 to +1)
3. PCR × ATR: E xpected move magnitude
4. Future Price: Close + (PCR × ATR)
High and low PCR×ATR rows are highlighted green for minimum value in the price forecast (buy signal) or red for maximum value in the price forecast (sell signal).
How to Use
1. Set UTC offset to your time zone for accurate future timestamps.
2. View PCR to assess bullish (positive) or bearish (negative) momentum.
3. Use PCR × ATR to estimate move strength and direction.
4. Reference Future Price for potential levels over upcoming intervals, and for buy and sell signals.
Limitations & Disclaimers
* This model uses linear extrapolation based on recent price behavior. It does not guarantee future prices.
* It uses only current bar data and no lookahead logic—compliant with Pine Script rules.
* Designed for analytical insight, not as an automated signal or trade executor.
* Best used on standard bar/candle charts (avoid non-standard types like Heikin‑Ashi or Renko).
Max Drawdown (Asset-Based Lookback)Max Drawdown (Long-Term Trading)
🟦 Majors BTC, ETH, BNB, LTC 180 – 365
Captures full correction cycles and recovery patterns (6–12 months).
🟩 Altcoins SOL, ADA, DOT, LINK, AVAX 90 – 180
Alts move faster than majors; 3–6 months catches most large swings.
🟥 Meme coins DOGE, SHIB, PEPE, FLOKI 60 – 120
Volatile with quick trend reversals; 2–4 months captures parabolic runs + drawdowns.
📅 Chart Timeframe:
Use Daily (1D) timeframe for all these.
For extra macro insight, try Weekly (1W) with 52 bars (≈ 1 year).
Compare multiple assets using the same period to assess relative risk.
If you're building a long-term portfolio, combine this with:
200-day SMA or EMA for trend context.
Sharpe Ratio or Sortino Ratio if you're looking for risk-adjusted return metrics.
IU Martingale StrategyDESCRIPTION
This strategy is a Martingale-based trading system that enters a long position based on simple candle patterns and doubles the position size after each loss. It tracks the worst-case drawdown using a live risk matrix table to help traders visualize the downside. With clear profit and loss points, auto position sizing, and a risk visualization table, it’s perfect for backtesting the Martingale concept directly on charts.
WHAT IS MARTINGALE SYSTEM?
The Martingale system is a position-sizing strategy where you double your trade size after every loss, aiming to recover all previous losses with a single profitable trade. It assumes that a winning trade will eventually occur. While it can be high-risk, it’s often used in controlled environments or with high probability setups.
USER INPUTS
📦 Starting Position: Initial position size for the first trade
📈 Profit Points: Target profit in price points
📉 Loss Points: Stop loss in price points
📊 Show Risk Matrix Table: Enable or disable a live table showing current streak and risk stats
LONG CONDITION
A long trade is triggered when the current candle closes higher than it opened, and no open position exists. The condition is simple and used to simulate market entry for testing the Martingale logic.
// Entry Condition:
close > open and no existing position
LONG EXIT
Each trade has a fixed stop-loss and take-profit, based on the user's input:
Stop Loss = Entry Price − Loss Points
Take Profit = Entry Price + Profit Points
The exit is handled using strategy.exit() with these dynamic values.
WHY IT IS UNIQUE
🔁 Implements a dynamic Martingale position sizing system
📊 Includes a visual Risk Matrix Table tracking losing streaks, max size, and risk
📈 Automatically resets position size after a win
🧠 Smart use of array to manage loss history and control logic
🎯 Customizable SL and TP for each entry
🎨 Beautiful chart visuals for entry, stop loss, and target levels
HOW USER CAN BENEFIT FROM IT
🧪 Test the sustainability of a Martingale system with real chart data
👁️ Visualize the risks and impact of consecutive losses
📈 Explore risk vs. reward scenarios before deploying in real markets
🧰 Use it as a base template to build more advanced Martingale or grid-based systems
📚 Great for educational purposes to understand the mathematical drawdowns involved in doubling strategies
Disclaimer :
This Video is not financial advice, it's for educational purposes only highlighting the power of coding( pine script) in TradingView, I am not a SEBI-registered advisor. Trading and investing involve risk, and you should consult with a qualified financial advisor before making any trading decisions. I do not guarantee profits or take responsibility for any losses you may incur.
مقارنة السعر مع تاريخ محدد + Table محمد الشمري✅ الهدف الأساسي من المؤشر:
عرض نسبة التغير في السعر بين:
السعر الحالي
وسعر سهم معين في تاريخ تحدده بنفسك داخل إعدادات المؤشر
ويتم عرض هذه النسبة في شكل مربع ثابت (Table) في أعلى يمين الشارت، بلون يعبر عن اتجاه التغير.
✅ Main Purpose of the Indicator:
The indicator titled:
"Price Change Compared to Specific Date + Table – Mohammad Al-Shammari"
Is designed to:
Display the percentage change in price between:
The current price.
And the price on a specific date you choose in the indicator settings.
This percentage is shown as a fixed table (box) in the top-right corner of the chart with a color that reflects the price movement direction.
8 AM & 9 AM NY Candle HighlighterThis indicator helps me to know when the 9am NY candle has closed above or below the previous candle.
Orthogonal Projections to Latent Structures (O-PLS)Version 0.1
Orthogonal Projections to Latent Structures (O-PLS) Indicator for TradingView
This indicator, named "Orthogonal Projections to Latent Structures (O-PLS)", is designed to help traders understand the relevance or predictive power of various market variables on the future close price of the asset it's applied to. Unlike standard correlation coefficients that show a simple linear relationship, O-PLS aims to separate variables into "predictive" (relevant to Y) and "orthogonal" (irrelevant noise) components. This Pine Script indicator provides a simplified proxy of the relevance score derived from O-PLS principles.
Purpose of the Indicator
The primary purpose of this indicator is to identify which technical factors (such as price, volume, and other indicators) have the strongest relationship with the future price movement of the current trading instrument. By providing a "relevance score" for each input variable, it helps traders focus on the most influential data points, potentially leading to more informed trading decisions.
Inputs
The indicator offers the following user-definable inputs:
* **Lookback Period:** This integer input (default: 100, min: 10, max: 500) determines the number of past bars used to calculate the relevance scores for each variable. A longer lookback period considers more historical data, which can lead to smoother, less reactive scores but might miss recent shifts in variable importance.
* **External Asset Symbol:** This symbol input (default: `BINANCE:BTCUSDT`) allows you to specify an external asset (e.g., `BINANCE:ETHUSDT`, `NASDAQ:TSLA`) whose close price will be included in the analysis as an additional variable. This is useful for cross-market analysis to see how other assets influence the current chart.
* **Plot Visibility Checkboxes (e.g., "Plot: Open Price Relevance", "Plot: Volume Relevance", etc.):** These boolean checkboxes allow you to toggle the visibility of individual relevance score plots on the chart, helping to declutter the display and focus on specific variables.
Outputs
The indicator provides two main types of output:
Relevance Score Plots: These are lines plotted in a separate pane below the main price chart. Each line corresponds to a specific market variable (Open Price, Close Price, High Price, Low Price, Volume, various RSIs, SMAs, MFI, and the External Asset Close). The value of each line represents the calculated "relevance score" for that variable, typically scaled between 0 and 10. A higher score indicates a stronger predictive relationship with the future close price.
Sorted Relevance Table : A table displayed in the top-right corner of the chart provides a clear, sorted list of all analyzed variables and their corresponding relevance scores. The table is sorted in descending order of relevance, making it easy to identify the most influential factors at a glance. Each variable name in the table is colored according to its plot color, and the external asset's name is dynamically displayed without the "BINANCE:" prefix.
How to Use the Indicator
1. **Add to Chart:** Apply the "Orthogonal Projections to Latent Structures (O-PLS)" indicator to your desired trading chart (e.g., ETH/USDT).
2. **Adjust Inputs:**
* **Lookback Period:** Experiment with different lookback periods to see how the relevance scores change. A shorter period might highlight recent correlations, while a longer one might show more fundamental relationships.
* **External Asset Symbol:** If you trade BTC/USDT, you might add ETH/USDT or SPX as an external asset to see its influence.
3. **Analyze Relevance Scores:**
* **Plots:** Observe the individual relevance score plots over time. Are certain variables consistently high? Do scores change before significant price moves?
* **Table:** Refer to the sorted table on the latest confirmed bar to quickly identify the top-ranked variables.
4. **Incorporate into Strategy:** Use the insights from the relevance scores to:
* Prioritize certain indicators or price actions in your trading strategy. For example, if "Volume" has a high relevance score, it suggests volume confirmation is critical for future price moves.
* Understand the influence of inter-market relationships (via the External Asset Close).
How the Indicator Works
The indicator works by performing the following steps on each bar:
1. **Data Fetching:** It gathers historical data for various price components (open, high, low, close), volume, and calculated technical indicators (SMA, RSI, MFI) for the specified `lookback` period. It also fetches the close price of an `External Asset Symbol` .
2. **Standardization (Z-scoring):** All collected raw data series are standardized by converting them into Z-scores. This involves subtracting the mean of each series and dividing by its standard deviation . Standardization is crucial because it brings all variables to a common scale, preventing variables with larger absolute values from disproportionately influencing the correlation calculations.
3. **Correlation Calculation (Proxy for O-PLS Relevance):** The indicator then calculates a simplified form of correlation between each standardized input variable and the standardized future close price (Y variable) . This correlation is a proxy for the relevance that O-PLS would identify. A high absolute correlation indicates a strong linear relationship.
4. **Relevance Scaling:** The calculated correlation values are then scaled to a range of 0 to 10 to provide an easily interpretable "relevance score" .
5. **Output Display:** The relevance scores are presented both as time-series plots (allowing observation of changes over time) and in a real-time sorted table (for quick identification of top factors on the current bar) .
How it Differs from Full O-PLS
This indicator provides a *simplified proxy* of O-PLS principles rather than a full, mathematically rigorous O-PLS model. Here's why and how it differs:
* **Dimensionality Reduction:** A full O-PLS model would involve complex matrix factorization techniques to decompose the independent variables (X) into components that are predictive of Y and components that are orthogonal (unrelated) to Y but still describe X's variance. Pine Script's array capabilities and computational limits make direct implementation of these matrix operations challenging.
* **Orthogonal Components:** A true O-PLS model explicitly identifies and removes orthogonal components (noise) from the X data that are unrelated to Y. This indicator, in its simplified form, primarily focuses on the direct correlation (relevance) between each X variable and Y after standardization, without explicitly modeling and separating these orthogonal variations.
* **Predictive Model:** A full O-PLS model is ultimately a predictive model that can be used for regression (predicting Y). This indicator, however, focuses solely on **identifying the relevance/correlation of inputs to Y**, rather than building a predictive model for Y itself. It's more of an analytical tool for feature importance than a direct prediction engine.
* **Computational Intensity:** Full O-PLS involves Singular Value Decomposition (SVD) or Partial Least Squares (PLS) algorithms, which are computationally intensive. The indicator uses simpler statistical measures (mean, standard deviation, and direct correlation calculation over a lookback window) that are feasible within Pine Script's execution limits.
In essence, this Pine Script indicator serves as a practical tool for gaining insights into variable relevance, inspired by the spirit of O-PLS, but adapted for the constraints and common use cases of a TradingView environment.
AnnualizedReturnCalculatorLibrary "AnnualizedReturnCalculator"
TODO: add library description here
calculateAnnualizedReturn(isStartTime, enableLog)
Parameters:
isStartTime (bool) : 开始时间的BOOL值变量（用于标记策略开始时间）
enableLog (bool) : 是否输出日志
Returns:
返回持仓基准年化收益率、资金基准年化收益率、总收益、平均资金占用
LiliALHUNTERSystem_v2📚 **Library: LiliALHUNTERSystem_v2**
This library provides a powerful target management system for Pine Script developers.
It includes advanced calculators for EMA, RMA, and Supertrend, and introduces a central `createTargets()` function to dynamically render target lines and labels based on long/short trade logic.
🛠️ **Main Features:**
– Dynamic horizontal & vertical target lines
– Dual target configuration (Target 1 & Target 2)
– Directional logic via `isLong1`, `isLong2`
– Integrated Supertrend validation
– Visual dashboard and label display
– Works seamlessly with custom indicators
🎯 **Purpose:**
The `LiliALHUNTERSystem_v2` Library enables Pine coders to manage and visualize targets consistently across all trading strategies and indicators. It simplifies target logic while maintaining visual clarity and modular usage.
⚠️ **Disclaimer:**
This script is intended for educational and analytical purposes only. It does not constitute financial advice.
Library "LiliALHUNTERSystem_v2"
ema_calc(len, source)
Parameters:
len (simple int)
source (float)
rma_calc(len, source)
Parameters:
len (simple int)
source (float)
supertrend_calc(length, factor)
Parameters:
length (simple int)
factor (float)
createTargets(config, state, source1A, source1B, source2A, source2B)
Parameters:
config (TargetConfig)
state (TargetState)
source1A (float)
source1B (float)
source2A (float)
source2B (float)
showDashboard(state, dashLoc, textSize)
Parameters:
state (TargetState)
dashLoc (string)
textSize (string)
TargetConfig
Fields:
enableTarget1 (series bool)
enableTarget2 (series bool)
isLong1 (series bool)
isLong2 (series bool)
target1Condition (series string)
target2Condition (series string)
target1Color (series color)
target2Color (series color)
target1Style (series string)
target2Style (series string)
distTarget1 (series float)
distTarget2 (series float)
distOptions1 (series string)
distOptions2 (series string)
showLabels (series bool)
showDash (series bool)
TargetState
Fields:
target1LineV (series line)
target1LineH (series line)
target2LineV (series line)
target2LineH (series line)
target1Lbl (series label)
target2Lbl (series label)
target1Active (series bool)
target2Active (series bool)
target1Value (series float)
target2Value (series float)
countTargets1 (series int)
countTgReached1 (series int)
countTargets2 (series int)
countTgReached2 (series int)
Entry Signal Paint (RSI + DMI + Stoch + MACD)RSI above 60
Stoch - cross overslod
DMI - Ungli
// === RSI Condition ===
rsi = ta.rsi(rsiSource, rsiPeriod)
rsiCondition = rsi > 60
// === ADX and DI Condition ===
adx = ta.adx(adxPeriod)
plusDI = ta.plus_di(adxPeriod)
minusDI = ta.minus_di(adxPeriod)
adxCondition = adx > 15 and plusDI > minusDI
// === Stochastic Condition ===
k = ta.stoch(close, high, low, stochK)
d = ta.sma(k, stochD)
stochOversoldCross = ta.crossover(k, d) and k < 20
ZScore Plot with Ranked TableVersion 0.1
ZScore Plot with Ranked Table — Overview
This indicator visualizes the rolling ZScores of up to 10 crypto assets, giving traders a normalized view of log return deviations over time. It's designed for volatility analysis, anomaly detection, and clustering of asset behavior.
🎯 Purpose
• Show how each asset's performance deviates from its historical mean
• Identify potential overbought/oversold conditions across assets
• Provide a ranked leaderboard to compare asset behavior instantly
⚙️ Inputs
• Lookback: Number of bars to calculate mean and standard deviation
• Asset 1–10: Choose up to 10 symbols (e.g. BTCUSDT, ETHUSDT)
📈 Outputs
• ZScore Lines: Each asset plotted on a normalized scale (mean = 0, SD = 1)
• End-of-Line Labels: Asset names displayed at latest bar
• Leaderboard Table: Ranked list (top-right) showing:
◦ Asset name (color-matched)
◦ Final ZScore (rounded to 3 decimals)
🧠 Use Cases
• Quantitative traders seeking cross-asset momentum snapshots
• Signal engineers tracking volatility clusters
• Risk managers monitoring outliers and systemic shifts
Z-Score Multi-Model ClusteringA price/volume clustering framework combining three market behavior models into a single indicator. Designed to help identify emerging trend strength, turning points, and volatility-driven entries or exits.
🔍 How It Works
This indicator classifies market states by comparing normalized price/volume behavior (via Z-Score) to different types of statistical or geometric "cluster centers." You can choose from three clustering approaches:
🧠 Clustering Models
1. Percentile (Z+CVD) – Trend Momentum Bias
Uses volume Z-Score + Cumulative Volume Delta (CVD).
Detects institutional pressure by clustering volume surges with directional delta.
Best for: Breakouts, momentum trades, volume-led reversals.
Cluster Colors:
🔹 Green triangle = Strong bullish confluence
🔻 Red triangle = Bearish divergence (bull trap risk)
⚪ Gray = Neutral/low conviction
2. Euclidean (Z+Slope) – Swing Mean-Reversion
Measures the angle of recent Z-score slope and compares it to directional cluster centers.
Helps detect early directional shifts or exhaustion.
Best for: Swing entries, pullback setups, exit timing
3. Hilbert Phase – Turn Detection via Signal Phase
Applies Hilbert Transform to the Z-Score, measuring the phase difference between trend and oscillator components.
Ideal for anticipating turns or detecting cyclical inflection points.
Useful for: Scalping, top/bottom spotting, volatility fades
✅ Features
Auto-updating cluster logic based on current data
Tooltips and clean user interface
Optional cluster bar coloring (can be toggled off)
Signal-only plotting keeps candlesticks readable
Clear entry/exit logic with triangle markers
Supports trend, swing, and oscillation-based systems
🛠️ Suggested Use Cases
Combine with VWAP, Session High/Low, or Liquidity Zones to confirm entry conditions.
Use Cluster 2 (strong bullish) on pullbacks to trend structure for add-on entries.
Use Cluster 1 in strong trends to watch for potential traps or exits.
Toggle models based on your strategy: e.g., Hilbert for scalping, Percentile for macro trend breaks.
🧪 Best Timeframes
Works across all markets and timeframes
For Percentile (Z+CVD), use intraday TF with 1m–5m CVD source
Hilbert and Euclidean preferred on 5m–1h for accurate slope/phase signals
⚠️ Notes
Clusters do not generate trade signals alone; use them in context with structure, VWAP, or trend filters.
Marker signals are filtered with a magnitude threshold to reduce noise.
Aggressive RSI + EMA Strategy with TP/SLWhat This Strategy Does 🔥
📉 It monitors RSI to find when the market is potentially oversold (RSI < 40) or overbought (RSI > 60).
📈 It checks the trend direction using two EMAs — fast EMA (short-term) and slow EMA (long-term).
✅ It only buys when the market looks oversold and the short-term trend is up (fast EMA > slow EMA).
❌ It only sells when the market looks overbought and the short-term trend is down (fast EMA < slow EMA).
💰 Once in a trade, it sets a take profit at 1% gain and a stop loss at 0.5% loss — so profits are locked and risks controlled.
🔄 This cycle repeats continuously, aiming to catch many small, quick moves rather than waiting for big swings.
🚀 The aggressive RSI thresholds mean it triggers trades more often — ideal for active traders who want lots of setups.
📊 It shows clear visual indicators and plots on the chart so you can easily see entries, exits, and indicator levels.
🔔 It also supports alerts, so you can get notified instantly when a trade setup happens.
In short, it’s a fast, trend-aware momentum strategy with built-in risk control designed for active trading and consistent small wins.
Multi-Crypto Principal Component AnalysisVersion 0.2
## 📌 Multi-Crypto Principal Component Analysis (PCA) — Indicator Summary
### 🎯 Purpose
This indicator identifies **cryptocurrency assets that are behaving differently** from the rest of the market, using a simplified approach inspired by Principal Component Analysis (PCA). It’s designed to help traders spot **cross-market divergences**, detect outliers, and improve asset selection and correlation-based strategies.
### ⚙️ How It Works
The indicator analyzes the **log returns** of up to 7 user-defined assets over a configurable lookback period (default: 100 bars). It computes the **z-score** (standardized deviation) for each asset’s return series and compares it against the average behavior of the group.
If an asset’s behavior deviates significantly (beyond a threshold of 1.5 standard deviations), it’s flagged as an **outlier**.
- Each outlier is plotted as a **colored dot horizontally spaced** above the price bar
- Up to **3 dots per bar** are shown for visual clarity
This PCA-style detection works in real time, directly on the chart, and gives you a quick overview of which assets are breaking correlation.
### 🔧 Inputs
- 🕒 **Lookback Period**: Number of bars to analyze (default: 100)
- 🔢 **Assets 1–7**: Choose any 7 crypto symbols from any exchange
- 🎨 **Colors**: Predefined per asset (e.g. BTCUSDT = red, ETHUSDT = yellow)
- 📈 **Threshold**: Internal (1.5 std dev); adjustable in code if needed
### 📊 Outputs
- 🟢 Dots above candles representing assets that are acting as outliers
- 🧠 Real-time clustering insight based on statistical deviation
- 🧭 Spatially spaced dots to avoid visual overlap when multiple outliers appear
### ⚠️ Limitations
- This is a **PCA-inspired approximation**, not true matrix-based PCA
- It does **not compute principal components or eigenvectors**
- Sensitivity may vary with asset volatility or sparse trading data
- Real PCA requires external tools like Python or R for full dimensional analysis
This tool is ideal for traders who want real-time crypto correlation insights without needing external data science platforms. It’s lightweight, fast, and highly visual — and gives you a powerful lens into market dislocations across multiple assets.
80% Rule BacktestStrategy Overview: 80% Rule Backtest Tool
This strategy tester is designed to validate the classic 80% Rule setup within a defined ETH futures session. Signals are triggered when price reenters the prior day's value area and holds for a qualified duration — targeting the Point of Control (POC) for primary exits, while tracking full value area traversals for research purposes.
- 📅 Session Logic: Anchored to a true 22-hour ETH futures window (5PM–3PM Pacific), with global time zone support
- 🧠 Signal Confirmation: Price must reenter and hold inside value area for ≥ 45 minutes to validate entry
Note: Optimized for 15-minute charts — aligns exactly with traditional rule definition (3-bar hold)
- 🎯 Targets: Primary TP at POC; visual logs for full VAH/VAL reach
- 🔍 Manual Override Mode: Precise SVP-level control when auto logic isn’t preferred
- 🔧 Debug Mode: Single-bar diagnostic labels for development and forward testing
This tool supports both auto-calculated and manually anchored value areas, allowing traders to blend systematic backtesting with discretionary insight.
working on this tool to be used as a strategy tester
Multi-TF Z-Score IndicatorIndicator to find the Z score for the daily 4h, 1h, 15m and 5 min time frames with 20 previous samples.
Simple BTC Breakout Bot (Works for sure)🚀 Simple BTC Breakout Bot (Works for sure)
This strategy is designed for BTC/USDT breakout trading using recent highs and lows. It automatically detects breakout signals and manages trades with Take Profit and Stop Loss to help manage risk and lock in profits.
Key Features:
🔍 Breakout Detection:
Enters a long position when price crosses above the highest high of the last N candles (default 20).
Enters a short position when price crosses below the lowest low of the last N candles.
💰 Risk Management:
Automatically sets a Take Profit (default 3%) and Stop Loss (default 1.5%) for every trade.
📊 Visuals:
Plots breakout levels (highest high in green, lowest low in red) on the chart.
🔔 Alerts:
Sends alerts on every breakout trade for long and short entries, compatible with TradingView’s alert system.
Parameters:
⏳ Breakout Lookback Period: Number of candles to check for breakouts (default: 20)
🎯 Take Profit (%): Profit target as a percentage from entry price (default: 3%)
🚫 Stop Loss (%): Maximum loss allowed as a percentage from entry price (default: 1.5%)
⚠️ Disclaimer:
This strategy is based on historical data and does not guarantee future profits. Always backtest and trade with proper risk management.
MA Crossover Strategy with TP/SL📊 MA Crossover Strategy with TP/SL
This strategy uses two simple moving averages (SMAs) to catch trend changes and trade breakouts with clear risk management.
🔥 How it works:
Enters a Long position when the fast SMA (short period) crosses above the slow SMA (long period), signaling an upward trend.
Enters a Short position when the fast SMA crosses below the slow SMA, signaling a downward trend.
🎯 Features:
Take Profit (TP): Automatically closes the trade at a defined percentage profit.
Stop Loss (SL): Limits potential losses with a predefined stop level.
Customizable parameters: Adjust the lengths of the moving averages, TP%, and SL% to fit your style.
Alerts: Receive notifications on every trade entry for timely action.
⚡️ Designed for traders looking for a simple, effective trend-following system with built-in risk control.
