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Fiverr Pro
English
$
USD
I will backtest and analyze your trading strategy using python
Automating trading strategies with Python, MQL5, and Pine Script
I specialize in algorithmic trading automation and quantitative development. While my professional background involves coordinating complex operations and managing financial tracking for global organi...
About this Gig
Thinking about using a trading strategy with real money?
Before risking your capital, it's important to see how your strategy performs on historical data.
I will backtest your trading strategy using Python and provide a clear report so you can better understand its strengths and weaknesses.
What I can backtest
Forex strategies
Gold (XAUUSD)
Crypto strategies
MT5 Expert Advisor logic
TradingView strategies
You'll receive
Trade summary
Win rate
Profit factor
Drawdown
Equity curve
Performance charts
Suggestions for improvement (Standard & Premium)
Please message me before placing an order so we can discuss your strategy and available historical data.
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.
