I will backtest your algorithmic trading strategy in python
Automating trading strategies with Python, MQL5, and Pine Script
About this Gig
Before you risk real capital on a manual strategy or an automated bot, you must prove that your logic has a mathematical edge.
I will build a custom Python engine to rigorously analyze your trading strategy against historical market data. I specialize in quantitative analysis strictly for the Forex, Cryptocurrency, and Commodities markets (such as XAUUSD).
What I will do for you:
- Translate your strategy logic (indicators, price action, time-based rules) into Python code.
- Run the strategy against high-quality, historical tick data.
- Provide a clear, actionable performance report.
Depending on your chosen package, your quantitative report will include:
- Win Rate & Profit Factor
- Maximum Drawdown (to understand your true risk exposure)
- Sharpe Ratio & Risk-Adjusted Returns
- Visual Equity Curve
Stop guessing and start trading with statistical proof. Send me a message with your strategy rules, and let's validate your edge!
Platform:
TradingView
•
MT5
•
Binance
Development technology:
Python
•
PineScript
•
MQL5
FAQ
Do I need to share my exact strategy rules with you? Is it safe?
Yes, I need your precise entry, exit, risk management, and indicator rules to code the simulation model. Your intellectual property is 100% safe, secure, and kept strictly confidential. I am more than happy to sign a Non-Disclosure Agreement (NDA) before we begin.
What asset classes do you specialize in for this service?
I specialize strictly in global Forex currency pairs, Cryptocurrencies (like BTC and ETH), and Commodities (such as Gold/XAUUSD). I focus heavily on these markets to ensure the highest quality historical data modeling.
If the data shows my strategy is profitable, can you turn it into a bot?
Absolutely. If the performance report reveals a strong mathematical edge and you want to fully automate it, we can easily transition into building a custom automated trading bot or Expert Advisor (EA) via my dedicated development gig.
How do you ensure the historical data analysis is accurate?
I use high-quality, institutional-grade historical data and clean it using Python. My testing architecture is designed to completely avoid common simulation traps like look-ahead bias or indicator "repainting," giving you realistic metrics you can actually trust.
