QAlpha StockTrend
- Эксперты
- Anh Quan Duong
- Версия: 1.0
- Активации: 5
QuanAlpha is pleased to introduce StockTrend, a sophisticated multi-symbols momentum-based expert advisor. StockTrend boasts several key features, including support for multiple symbols, robust performance in both bull and bear markets, and effectiveness across various trending assets beyond stocks. This expert advisor operates on a simple yet powerful logic: identifying trends, capitalizing on momentum, and exiting positions when momentum diminishes.
Key Features:
- Multi-Symbol Support: Accommodates trading across multiple symbols simultaneously, providing diversification opportunities within a single trading strategy.
- Market Versatility: Strong performance in both bullish and bearish market conditions.
- Risk Management: Incorporates stop-loss (SL) and trailing stop-loss (trailing SL) mechanisms to mitigate risk exposure. No fixed TP targets in favor of fully capturing market impulses.
- Trading Frequency: Executes trades approximately two to three times per week per symbol, with frequency influenced by prevailing market conditions.
How to use the EA:
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Chart Setup:
- Install StockTrend on a chart ofhighly liquid stock, such as AAPL or TSLA.
- Enable algorithmic trading functionality for seamless execution.
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Symbol Selection:
- Define the list of trading symbols using the CUSTOM_SYMBOLS parameter, ensuring proper separation with commas (e.g., AAPL, TSLA, USDJPY, XAUUSD).
- Risk Management:
- Tailor risk exposure according to individual risk preferences:
- High-Risk Setting: Set RISK_PER_TRADE to 0.01.
- Medium-Risk Setting (Recommended): Utilize a setting of 0.005, consistent with backtesting and forward-testing outcomes.
- Ultimate Peace of Mind: Opt for a conservative approach with RISK_PER_TRADE set to 0.002.
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Backtesting:
- Make sure you backtest every symbols you intend to trade with this EA
- Supports multi-symbol backtesting.
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Modeling Quality:
- Choose the appropriate modeling method based on available data quality:
- Opt for "1 minute OHLC" or "every tick" modeling for robust testing outcomes.
- Alternatively, utilize high-quality tick data for enhanced accuracy by selecting "every tick based on real ticks."
- Choose the appropriate modeling method based on available data quality:
Recommended account size: 1000$ or above
Join our telegram support group: https://t.me/+23-8VLtDpUs1NzBl
