feat(quant): configurable exit params TP/SL/trail + OOS trail experiments - #237
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feat(quant): configurable exit params TP/SL/trail + OOS trail experiments#237JoTalbot wants to merge 52 commits into
JoTalbot wants to merge 52 commits into
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Deployed CatBoost direction model was degenerate (prob_up=0.433 for 30/35 assets, AUC 0.504, never reaches the 0.65 entry gate). Replace with a model trained on scale-free features with a strict per-symbol walk-forward split: AUC 0.533, hit-rate 81-83% at prob>=0.65 on two independent OOS windows, positive net PnL with Directional v2 paper exit rules. Old model kept as fallback; ml_predictor prefers catboost_price_dir_v2.cbm.
RLSignalBridge vs training env (kg_v8) mismatches: 1. onehot always marked BTC - now per-asset index in ASSET_ORDER (32 assets, alphabetical, as MultiAssetEnv); unknown asset -> None (no signal). 2. static feature 3 was vol_chg, training used vol_ratio - fixed. 3. action was not clamped to [-1,1] like in training rollout - pos could be -0.5 (outside [0,1]) - fixed. Dead tickers MATIC/RNDR (renamed to POL/RENDER) removed from ML signals, RL default universe and notebook generator template. rl_signals.json refreshed (9 assets, honest FLAT verdicts). 61 quant/ml tests pass.
…ction Data: - scripts/quant_backfill_history.py: paginated Binance klines (+Bybit fallback for KAS), refreshes stale tails first; 17 assets extended to ~5500 bars. Signal product (generate_quant_signal_product.py): - _latest_rows now picks the most complete FRESH series (staleness >2h skipped, fixes delisted TON on Binance shadowing live Bitstamp data); - regime computed on the last CLOSED bar (in-progress bar had partial volume -> false illiquid); NO_DATA dropped 16 -> 0. ML (quant_ml_eval_train.py): - engine-style simulation now executes at trigger levels (conservative), not at piercing bar closes; retrained on full dataset: AUC 0.513 -> 0.536, hit@0.65 = 82.4%, +31.6% net on OOS sim at thr 0.65. RL (quant_train_ppo.py + rl_signal_bridge.py): - train LSTM-PPO v9 1:1 with kg_v8 methodology (300 episodes, GAE, clip 0.2) on local data, universe POL instead of delisted MATIC; - validation: sum_rl +96.0% vs Buy&Hold -114.0% (v8: +51.4%); - bridge reads asset names from checkpoint (supports v8 MATIC and v9 POL), MODEL_FILE -> ppo_v9.pt; signals refreshed (10 assets, honest FLAT veto). - 61 quant/ml tests pass.
- collect_orderbook_snapshots.py: fix kucoin depth (20/100 only), add okx/bitstamp/coinbase to the collector (binance,kucoin,mexc,okx,bitstamp, coinbase); unit interval 30s -> 15s. Collection rate ~3x faster. - scripts/analyze_orderbook_data.py (new): per-exchange spread/depth stats + cross-exchange mid disparity windows (read-only); report -> data/reports/ orderbook_analysis.json. - Market-making simulator first run (>=200 snapshots): fill_rate 63-96% but negative PnL - naive passive MM suffers adverse selection; full run needs >=1000 snapshots/pair (binance/mexc ~40% there, ~1.5h to go). - docs/QUANT_SIGNAL_PRODUCT.md: 33 assets, backfill, fresh-series selection, closed-bar regime, ML v2 / PPO v9 summary. - docs/PROJECT_INVENTORY.md regenerated.
…ng brief - scripts/run_market_making_simulator_v2.py (new): inventory-aware MM research (fills only on favorable moves, single-position rule, no stacking). First run confirms naive MM economics: maker fee 0.1% (10bps) exceeds median spreads (BTC 0.002bps, ETH 0.05, SOL 1.3) -> spot MM on top pairs needs rebate programs or wider-spread universe; documented in report. - run_morning_brief.py: read-only Quant WATCH section (WATCH_UP/WATCH_DOWN from quant_signal_product.json, max 5) after the crypto PnL line.
Old paths (ppo_trader.pt, ppo_multi_24.pt, catboost_price_dir.cbm) are superseded by ppo_v9.pt and catboost_price_dir_v2.cbm; removed stale entries.
…train timers
- scripts/quant_watch_backtest.py (new): replays signal-product WATCH rules
on OOS tail; result: WATCH_DOWN precision 59.4% (85/143) - moderate edge
vs 50% baseline; WATCH_UP produced 0 signals (rule too strict in current
down-market) - documented, no rule changes.
- scripts/quant_ml_monitor.py (new): prob_up distribution stats, signal/CSV
freshness, drift vs previous snapshot; history in
data/reports/quant_ml_monitor_history.json; status OK/WARN.
- scripts/quant_ml_feature_experiment.py (new): 13 base vs 21 extended
features on same OOS; extended is NOT better (AUC 0.5326 vs 0.5355,
hit@0.65 70.8% vs 82.2%) - base feature set stays.
- deploy/systemd/aios-quant-ml-retrain.{service,timer}: weekly retrain
(Mon 04:00, deploy-only-if-better guard already in the script).
- deploy/systemd/aios-quant-ml-monitor.{service,timer}: hourly drift monitor.
- Both timers installed and enabled on the host.
Critical methodology fix: v8/v9 validation lacked the action clamp used by
the deployed bridge; act < -1.5 silently became SHORT positions (-0.5) that
are impossible in the discrete {0, 0.5, 1} policy. Those historical
profits (v9 +96%) were artifacts of hidden shorts.
- quant_train_ppo.py / quant_train_ppo_v10.py: val_on_asset now clamps
act to [-1,1] exactly like rl_signal_bridge.py.
- quant_train_ppo_v10.py (new): honest walk-forward split - env trains on
first 70% of each asset, validates on the unseen last 30% (gap 48).
- Honest result: deployed ppo_v9 is a pure FLAT agent on OOS (sum_rl 0.0
vs Buy&Hold -233%) - value is loss avoidance, not earning. v10 not
deployed (identical FLAT, no OOS edge under deployed action space).
- quant_ml_horizon_experiment.py (new): h1 label optimal (AUC 0.5355,
hit@0.65 82.2%); h4/h8/h24 worse - next-bar model stays.
- Reports: ppo_oos_honest.json, ppo_v10_oos_eval.json,
quant_ml_horizon_experiment.json.
Replays the production algorithm (ML gate, owner risk profile, TP/SL/trail exits, fees) as if trading started exactly one month ago, synchronous per-bar processing of all 33 symbols with a single 000 portfolio. Result (2026-07-14 -> 2026-08-14): - current algorithm: -0.30% (1 trade; ML>=0.65 gate blocked 1948 entries) - control (no ML gate): -0.54% (5 trades, 2 wins) - market Buy&Hold: mean -9.30%, best ADA +9.00%, worst BONK -37.73%, BTC -2.71% The algorithm beat the average currency by +9.0pp and BTC by +2.4pp, i.e. the gate acted as capital protection (mostly cash), not an earner.
Re-run for 3/6/12 calendar months. The deployed algorithm holds cash in all windows (ML>=0.65 gate almost never fires; after the first -1.5% loss the 0.25% DD kill blocks further entries), so portfolio PnL is -0.30% in every window while the market fell: mean currency -30.75% (3m), -24.11% (6m), -68.32% (12m). BTC -46.7%, ETH -58.1%, ADA -80.0% over 12m. The gate acts as capital protection (beats average currency by +9..+68pp) but never earns: cash (0%) beats the algorithm by 0.3pp in every window.
Systematic research over 16 strategies on honest OOS (no lookahead, 0.25% per-side costs, ML retrained on train window only, equal-weight symbols): - Winner: daily SMA50/200 long/short cross-following: 70/30 split +7.8..+11.2% (params 40/160, 50/200, 60/240 all positive); 50/50 split +34.7% (50/200) / +19.7% (60/240), both OOS halves positive. - Long-only variant of the same rules: -4.5% -> profit comes from shorts. - XS mean-reversion unstable (bot3_p7: +9.6% then -12.9%); RSI daily MR weak (+1.8..+5.6%); ML long/short and inverted-ML negative on OOS. - Report: data/reports/strategy_research_summary.md + JSON artifacts. Caveats: funding/borrow costs not modeled; profit concentrated in the bear regime; constitutional gate requires owner decision for real shorts.
…earns Backtest engine fix (v2): equity now compounds bar by bar (earlier version summed arithmetic returns, which overstates PnL on volatile assets - e.g. sum -97.96% vs actual -71.3% for BONK) and the position adopts the signal at the same bar close (true next-bar semantics). Corrected results for daily SMA50/200 long/short with 0.25%/side costs: OOS30: +12.62% net (funding base), +1.68% (funding stress) OOS50: +41.56% net (base), +23.92% (stress); half1 +32.4%, half2 +6.5%; 24/33 symbols positive. Both halves and all split x funding combos > 0. MA_LS_60_240: +14.0% (OOS30), +29.9% (OOS50). All long-only variants and Donchian/XS stay negative -> shorts are the edge. Report: data/reports/earn_research_summary.md + JSON.
Findings: aios-groq-key restart loop (missing script, 7816 restarts), disk 83%, context/runtime entry_mode drift, quant exchange_not_allowed=96, 31 unpushed commits. No code or runtime changes.
aios-groq-key.service pointed at groq_key_retry.py which exists neither in the filesystem nor in git history; the unit had accumulated 7832 restarts (~2833/day). Its function is already covered by the live successor aios-groq-autopilot.timer (hourly, status ok, 8 keys). Unit stopped, disabled and masked; base unit preserved in deploy/systemd and a runtime backup. Masks snapshot and its inventory test updated accordingly. Disk 83% -> 81% by removing only disposable data (apt cache, crash dumps, disabled snap revisions, pip/pip-tools caches, stale /tmp, __pycache__). AIOS backups were left intact - all within the projects own retention policy. cleanup_disk.sh now also prunes regenerable caches so this does not recur. Main consumers (ollama 14G, docker 17G) are in active use and deliberately untouched - owner decision. PROJECT_CONTEXT corrected: runtime entry_mode is enabled under the owner-approved constrained paper profile, not freeze; documented the verified quant slice (18h, trades=0, exchange_not_allowed=96/scan). Verified: systemctl --failed empty, audit_deployment_sources drift 0, tests/test_systemd_inventory.py 3 passed, ruff clean, health 21/22.
…est (bounty alerts)
Голосовые сообщения и TTS-ответы больше не остаются на диске: скачали из Telegram - распознали (whisper/gemini) - удалили /tmp/aios_tg_*. Архивные аудио вне /tmp (Calls/) не затрагиваются.
…, android images, backups prune)
…02-15..2026-08-15)
…tate extended to 8760 bars
…est on real kucoin/bitstamp prices
…cklist) + 1y results
…ive on 4 walk-forward windows - scripts/quant_oos_profit_experiments.py: 4-fold expanding-window walk-forward, fresh CatBoost v2 per fold (train-calibrated ML threshold), a-priori variant battery, dedup of overlapping windows, portfolio sim under prod profile. - Unique trades across OOS windows: BASE +7.15$ (WR 50%, PF 2.50) vs hard trail 1.0 +14.42$ (WR 87.5%, PF 5.83); monotonic trail 0.988<0.995<1.0. Paper-test recommended, live stays forbidden. - docs/TRADING_OOS_PROFIT_EXPERIMENTS_2026-08-15_RU.md; raw run artifact: data/reports/oos_profit_experiments.md (gitignored).
…rail_ratio=1.0 - DirectionalV2Config gains take_profit_pct/stop_loss_pct/trail_ratio, read from env (AIOS_QUANT_TAKE_PROFIT_PCT / AIOS_QUANT_STOP_LOSS_PCT / AIOS_QUANT_TRAIL_RATIO); defaults preserve legacy behaviour (2.0% / -1.0% / 0.988) - fail-closed. - quant_directional_v2 exit logic uses config values instead of literals. - tests: trail_ratio env controls trailing stop (parametrized), TP/SL env thresholds, defaults preserve legacy behaviour. tests/test_quant_directional_v2.py +22 passed. - deploy/systemd/aios-quant-trading.service: AIOS_QUANT_TRAIL_RATIO=1.0 (hard trail) for the owner-approved constrained paper profile per OOS findings (WR 87.5%, PF 5.83). Runtime application requires operator approval (given).
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Восстановленный orphan-PR: работа без PR с 2026-08-15. +52/−64 vs main.
Этот PR создан ИИ-агентом (OpenHands) от имени оператора при наведении порядка в ветках.