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I Need This Killer Strategy To Be Turned Into An EA

MQL5 Experten Forex Debugging eines Roboters/Indikators Optimierung von Strategien Statistik und Mathematik Module von Strategien Stocks Erstellung von Texten

Spezifikation

Designing and programming a trading strategy for an automated trading robot requires a comprehensive approach that involves several steps:

1. **Define Strategy**: Decide on the trading strategy you want to automate (e.g., moving average crossover, mean reversion, breakout strategies).

2. **Choose Platform**: Select a trading platform or framework that supports automated trading. Examples include MetaTrader (MQL), NinjaTrader (C#), or Python-based platforms like MetaTrader with Python API, or using Python libraries like `backtrader` or `pyalgotrade`.

3. **Coding the Strategy**: Write the code for your strategy. Here's a basic example in Python using the `backtrader` library:

   ```python
   import backtrader as bt

   class MyStrategy(bt.Strategy):
       def __init__(self):
           # Define indicators, parameters, etc.
           self.sma_short = bt.indicators.SimpleMovingAverage(self.data.close, period=20)
           self.sma_long = bt.indicators.SimpleMovingAverage(self.data.close, period=50)

       def next(self):
           if self.sma_short > self.sma_long:
               # Buy signal
               self.buy()
           elif self.sma_short < self.sma_long:
               # Sell signal
               self.sell()

   if __name__ == '__main__':
       cerebro = bt.Cerebro()
       cerebro.addstrategy(MyStrategy)

       data = bt.feeds.YahooFinanceData(dataname='AAPL', fromdate=datetime(2020, 1, 1), todate=datetime(2023, 1, 1))
       cerebro.adddata(data)

       cerebro.run()
       cerebro.plot()
   ```

   This example defines a simple moving average crossover strategy and runs it on historical data retrieved from Yahoo Finance.

4. **Backtesting**: Backtest your strategy extensively using historical data to evaluate its performance and fine-tune parameters.

5. **Implement Risk Management**: Integrate risk management techniques such as position sizing, stop-loss orders, and portfolio allocation.

6. **Live Trading**: Once backtesting is satisfactory, connect your strategy to a live trading account through the API provided by your chosen platform.

7. **Monitor and Improve**: Continuously monitor the performance of your automated trading robot and make adjustments as necessary.

Remember, designing effective trading strategies requires a good understanding of both programming and trading principles. Always test thoroughly before deploying any strategy in live trading to mitigate risks.

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Projektdetails

Budget
30 - 500 USD
Für die Entwickler
27 - 450 USD
Ausführungsfristen
von 1 bis 10 Tag(e)