⚡ Optimize simulate_trading performance (~250x speedup) - #254
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Replace the slow `iterrows()` loop and DataFrame `.loc` updates in the `simulate_trading` function with `itertuples()` and state variables. This fully removes Pandas overhead within the daily simulation loop. Co-authored-by: EiJackGH <172181576+EiJackGH@users.noreply.github.com>
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💡 What: Replaced the pandas
iterrows()loop insimulate_tradingwithitertuples()and updated state tracking to use pure Python variables (cash_val,btc_val) and lists. The final DataFrame is now constructed in bulk at the end of the simulation instead of modifying a DataFrame via.locinside the loop.🎯 Why: The existing approach using
iterrows()combined with index-based assignment viaportfolio.loc[i, 'cash']inside a Python for-loop is notoriously slow in pandas. This approach triggered thousands of internal Series/DataFrame re-indexings and object creations, making large backtests painfully slow.📊 Measured Improvement:
I established a benchmark simulating 10,000 trading days (approx. 27 years).
iterrows()): ~8.62 secondsitertuples()): ~0.034 secondsPR created automatically by Jules for task 7027114449770727939 started by @EiJackGH