A Python rebuild of this AI BTC strategy came out at 1,001% returns and matched the CoinQuant equity curve almost exactly, which is the main validation result in this test.

In this video, we take the BTC strategy from the earlier CoinQuant AI backtest and reproduce it step by step in Python, then compare the signals, trades, commissions, and equity curve until the results line up. The goal here is not to hype a perfect system, but to validate the logic, show where small differences come from, and set up the next Monte Carlo stress test properly.

🔧 What you’ll see in this video:
→ the BTC backtest result in Python versus CoinQuant AI
→ the exact strategy rules: HMA 16 and 64, RSI 14 above 52, linear regression filter
→ how backtesting.py compares with a custom local backtester
→ why commissions created most of the remaining curve difference
→ the trade count comparison of 131 trades in Python versus 132 in CoinQuant
→ how the indicators and entry and exit signals were validated visually
→ why the next step is a Monte Carlo stress test for robustness

╔═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦══╦═╦═╦══╦═╦═╦══╦═╦═╦
🔗 Resources & Links:
————————————–
• 📚 My Algorithmic Trading Courses → https://codetradingcafe.com
• 🤖 Free Tier CoinQuant Account: https://app.coinquant.ai/?ref=nZtzOwut
• 📘 My Book: “Algorithmic Trading with Python” → https://a.co/d/6woMBHt
• 💻 Free Python Code (GitHub) → https://github.com/ZiadFrancis/CoinQuant_Vs_Python_HMA_Strategy

Happy learning, happy coding ☕
╚═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩

If you want more step-by-step AI trading tests like this, subscribe and comment with the asset you want me to run next.
#aitrading #montecarlo #btc

0: 00 Python Backtest Challenge
0: 38 Strategy Rules Recap
1: 59 Backtester Setup
4: 01 CoinQuant Results Comparison
5: 01 Custom Backtester Validation
6: 02 Trade Export Debugging
8: 03 Matching Equity Curves
9: 00 Commission Difference Found
10: 23 Final Parameters And Next Step