Just finished backtesting a Fibo H4 strategy on USTEC. 6y data, 60.3% win rate. Thoughts on these metrics?
u/iam_warrior ·
Reddit — r/algotrading
· July 01, 2026 at 09:39
· ⬆ 15 pts
· 💬 48 comments
| View on Reddit ↗
AI Summary
Summary
The author backtested a Fibonacci-based H4 strategy on USTEC (Nasdaq 100) over 6 years, reporting exceptional metrics: 60.3% win rate, 260% return, and only 3.5% max drawdown.
Despite impressive numbers, the author is skeptical of over-optimization and acknowledges a corrected Sharpe ratio (3.40) and issues with walk-forward/OOS validity.
The post is a request for peer review on strategy sustainability, not a recommendation to trade any current setup.
Score15
Comments48
Upvote %74%
▶ Full Post Text
Hey everyone,
Been tweaking a deterministic pattern setup on USTEC H4 over a 6-year history (started with a $10k mock account) and the equity curve turned out surprisingly clean. I’m honestly a bit skeptical whenever a backtest looks this linear, so I wanted to throw the numbers here and get some brutal feedback.
Quick summary of the stats from the run:
Total return sits at 260.60% ($36,056 final equity) with a 60.3% win rate over 574 trades. Profit factor is 2.77.
What's catching my eye is the max drawdown, it's only 3.50%. For that kind of return, a 3.5% DD feels almost too good to be true, though the Sharpe ratio is kinda mid at 0.44.
I've also been trying out the built-in AI assistant on this app to filter my live sessions based on daily market states. Like right now, it's flagging H1 as pure indecision/consolidation due to a bunch of Dojis, so it helps me decide whether to skip the day or trust the macro trend.
For anyone who trades Nasdaq/USTEC or index CFDs regularly, does this look sustainable or am I missing some hidden pitfall here? Maybe over-optimization?
Let me know what you guys think, appreciate any insights!
**Edit:**
**the Sharpe Ratio is wrong: the actual corrected is: 3.40**
**the WALK-FWD and OOS return is wrong. it should not show in deterministic rule. is should show when use custom train model.**