Debugging

When V5 went live, every single inference run logged the same warning: WARNING: 13 features contain NaN values. Filling with 0. I saw it on day one. I told myself it was a data warmup issue — the live feed just needed more bars to stabilize. That was wrong. Same Code, Different Data V5 solved the training/live code divergence problem from V4. One shared feature_core.py. Every pipeline — training, backtest, all five live scripts — imports from the same file. ...

Day 3. Equity went from $701 to $928. Not because the market was kind. Because the data was finally clean. One line of code. Running for two days. Something felt off on day one. I was holding long positions, the market was moving up, but my equity was bleeding faster than it should. I couldn’t find the cause. On day three, I pulled the logs. Found a keyword repeating every 15 minutes: ...

Today I ran a full cross-audit of the V5 quant system. Scope: training, backtest, and live execution — 13 files, 6,500 lines. I was looking for drift. I found something worse. The Trailing Stop Was Updating. It Was Never Checking. My backtest engine has four exit paths: Fixed stop-loss Fixed take-profit Trailing stop Momentum fade SL and TP are delegated to the exchange. The trailing stop has to be managed by the script itself. ...

My V4 trading system used 11 data sources: price data, funding rates, open interest, institutional long/short ratio, liquidation data, fear & greed index, CVD, and more. The backtest results looked solid. Win rates above 80%. Drawdown under 15%. Then I audited the code line by line. What I found made me rebuild the entire system from scratch. Bug 1: The Fear & Greed index was always 50 The Alternative.me API returns data in this format: ...