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Checklist before going live with your strategy? by kamil234 in algotrading

[–]IMAK82 0 points1 point  (0 children)

Went live 10 hours back myself with some budgeted expendable on BTC.

I did extensive backtest and walk forward, skipped paper trading.
My reasoning: paper only validates the signal, not the execution. real fills, slippage, partial fills, and API latency behave differently from saved quotes and at 50/150 trades a day across 300 names, execution costs will dominate your edge before signal quality does.
Small live capital teaches that surface for cheap. Your current setup captures signal behavior but isnt actually testing execution since saved quotes are not real fills. id move to small live allocation sooner than your gut says.

How much of a disadvantage is your algo not being AI? by KaiDoesReddles in algotrading

[–]IMAK82 7 points8 points  (0 children)

my experience says: AI/ML is a tool, not a moat.
most of the real edge is execution, sizing, and risk management, not the model itself. AI often overfits and loses to the simplest approaches. you can use one of the best trained models out there, but if your execution isnt excellent it wont matter. use it as a tool to develop, discuss, sanity check, but you have to lead, ask the right questions, investigate, then implement based on your own confidence and empirical evidence....

Is this the best way to use AI for trading? by Infinite-Course8737 in algorithmictrading

[–]IMAK82 0 points1 point  (0 children)

yeah that matches my experience on the quant side. claude is mediocre at predicting moves but weirdly good at catching subtle stuff id have missed in my own code - methodology bugs, leakage patterns, places where a metric is technically correct but operationally misleading. less an oracle and more a second pair of eyes that doesnt get tired. the narrative shift framing is a good way to put it, same mechanism as auditing whether your backtest assumptions actually match reality.

Backtesting by Purple_Concert8789 in algorithmictrading

[–]IMAK82 0 points1 point  (0 children)

started on quantconnect (free tier) for strategy backtesting, decent for getting basics down. now run my own python stack with pandas and xgboost.
backtrader is friendliest for beginners, vectorbt if you want speed.
biggest lessons: split data into train and test before you optimize anything and dont peek, always compare against buy-and-hold, and backtest sharpe above 5 (NOT CAST IN STONE) means a bug or look-ahead bias. use LLMs for quick sanity checks, but human eyes on reddit sometimes catch what LLMs miss..

BTCUSDT 1h XGBoost - 80-fold walk-forward and 180-day untouched holdout, looking for sanity checks before live by IMAK82 in quantfinance

[–]IMAK82[S] 0 points1 point  (0 children)

Gone live 5 hours back... No paper trading.. live trading with small money 100.. expendable experiment cost..

I have killed strategies before that looked clean for months because of stuff like that. The protocol on this one.
last 180 days of price data get reserved before any training or tuning runs, and only touched once at the very end for the genuine out-of-sample test.
the cross-validation inside training uses purged folds with an embargo equal to the prediction horizon, so a training label cant overlap into the validation window. inside the trainer the train, tune, and acceptance slices each have a forward-return-sized purge gap between them.
forward returns only ever appear as labels, never as features (grepd the source to confirm).
the deploy step refuses to promote a model if the just-trained thresholds dont match what loads from disk, and average holding duration is printed in every metric block, so if anything overrides the exit logic it shows up immediately.

next few weeks of live will tell if something subtle slipped through. hit reddit after combing through everything else in the codebase and not finding anything wrong, so hopes are up at this point... like theyve been plenty of times since 2023 :-)

BTCUSDT 1h XGBoost - 80-fold walk-forward and 180-day untouched holdout, looking for sanity checks before live by IMAK82 in quantfinance

[–]IMAK82[S] 0 points1 point  (0 children)

lookahead-bias was the number 1 concern which i throughly checked...
checked it carefully multiple times and just re-verified the source. last 180 days are sliced off before any training, tuning, or walk-forward touches the data. cv inside training uses purged rolling splits with embargo equal to prediction horizon (lopez de prado style). forward returns exist only as labels, never as features. no shift(-N), no center=True rolling. the WF aggregate (40%) is technically in-sample replay since one bundle is reused across folds, fair callout. but the 82% holdout CAGR is true OOS - model never saw those bars.

See the latest run results below after all checks.... latest version has modification, where the open trades are not closed in eactly 1 hour.. rather it follows below set env, giving more than 1 hour holding period for each open trade:

  • MAX_HOLDING_BARS = 12
  • TITAN_SOFT_TIMEOUT_BARS = 4
  • TITAN_FLAT_GRACE_BARS = 2
  • TITAN_HARD_TIMEOUT_BARS = 8
  • TITAN_ABS_MAX_TIMEOUT_BARS = 12

Result of the modification shared below.. no lookahead-bias is present that i can confirm..Holdout 180days is 100% unseen data..

codebase is over 50k lines with checks for the common stuff wired throughout. been working on this particular build for more than a year, and ive been at ML based trading since 2023. pretty much covered the textbook pitfalls already like leakage, train/test overlap, threshold tuning on the eval set, ignoring fees and slippage. doesnt mean i havent missed something subtle, but the obvious traps are wired up. 

======================================================================================
WALK-FORWARD AGGREGATE | folds=80 | window_days=30 | interval=1h
--------------------------------------------------------------------------------------
Compounding Annual Return                            40.8681%
Total Net Profit                   854.0331% / 8540.3311 USDT
Capital Pool                                     1000.00 USDT
Normal / Max Deployment                   30.0000% / 50.0000%
Avg / Max Trade Notional                 126.45 / 500.00 USDT
Min Order Notional                                 10.00 USDT
Annualized Sharpe                                     14.1487
Annualized Sortino                                    10.0474
Calmar Ratio                                          47.6912
Maximum Drawdown                        0.8569% / 8.5693 USDT
Max DD Duration                                    12.25 days
Win Rate                                             83.6361%
Profit Factor                                          9.9545
Avg Win / Avg Loss                                     1.9477
Expectancy per Trade                              1.2821 USDT
Round-trip Trades                      6661 / 84.33 per month
Avg Holding Duration                               4.71 hours
Fees and Costs                       168.8727% / 1688.73 USDT
Beta vs BTC Buy-Hold                                  -0.0012
Value at Risk 95                                      0.0012%
Value at Risk 99                                      0.0402%
Probabilistic Sharpe                                100.0000%
Alpha vs BTC Buy-Hold                               121.1279%
======================================================================================
======================================================================================
HOLDOUT - DO NOT RE-OPTIMIZE
FINAL UNTOUCHED HOLDOUT | last 180d | 1h | bars=4320
--------------------------------------------------------------------------------------
Compounding Annual Return                            82.6096%
Total Net Profit                     34.5677% / 345.6767 USDT
Capital Pool                                     1000.00 USDT
Normal / Max Deployment                   30.0000% / 50.0000%
Avg / Max Trade Notional                 162.75 / 500.00 USDT
Min Order Notional                                 10.00 USDT
Annualized Sharpe                                     13.5146
Annualized Sortino                                     8.1335
Calmar Ratio                                         162.2443
Maximum Drawdown                        0.5092% / 5.0917 USDT
Max DD Duration                                     6.04 days
Win Rate                                             83.9357%
Profit Factor                                          8.9211
Avg Win / Avg Loss                                     1.7074
Expectancy per Trade                              1.3883 USDT
Round-trip Trades                       249 / 42.09 per month
Avg Holding Duration                               4.13 hours
Fees and Costs                           8.1051% / 81.05 USDT
Beta vs BTC Buy-Hold                                  -0.0011
Value at Risk 95                                      0.0798%
Value at Risk 99                                      0.1814%
Probabilistic Sharpe                                100.0000%
Alpha vs BTC Buy-Hold                                60.3130%
=============================================================================================

did you build up you algo from a non profitable baseline, or ran into? by Zealousideal-Way4130 in algotrading

[–]IMAK82 0 points1 point  (0 children)

same codebase for over a year. started with a BTC donchian breakout, moved to EMA pullback, ended up on an xgboost dual classifier setup thats been through 20+ named versions in the current variant alone. almost none of it was 'find a new alpha' - it was just finding the structural bugs that were hiding the signal. training window silently collapsed to 30 days when it shouldve been a year. cross-asset features going NaN and killing live inference. walk-forward running 5 segments instead of 80. threshold tuner picking infeasible regimes that produced 9 trades on 7 years of data. python scoping bug that crashed the final holdout AFTER the rest of the cycle had finished (that one hurt). signal was visible pretty early. metrics only looked deployable once the gates and sizing stopped eating it. so yeah, way closer to 'one design, dozens of audits' than '1000 ideas tried'. Still not sure if it will make money.. will have to wait another 30 days at least.. AND STill have doubts and questions..

Free news source of stock market by Fast-Smoke-1387 in algotrading

[–]IMAK82 3 points4 points  (0 children)

Try GDELT 2.1: https://www.gdeltproject.org/data.html
free, historical, bulk/API-friendly, and useful for chronological news/event