Claude/continue previous work vvwws6 - #346
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Adds examples/analyze_market.py with three modes: - forecast: probabilistic forecast with sample-path quantile bands, plot + CSV - backtest: walk-forward model evaluation (directional accuracy, MAE/RMSE/MAPE) plus a forecast-driven long/flat strategy vs buy-and-hold - signal: decision-support JSON report (trend, support/resistance, implied return, dispersion-based confidence) Supports local model/tokenizer dirs for offline use and configurable CSV column mapping. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Snkpa9NxHpLnBphcE5Luy8
…_market Pulls daily OHLCV for US tickers/ETFs into the CSV schema analyze_market.py expects, sourcing stockanalysis.com with a Nasdaq API fallback. Enables running Kronos forecast/signal/backtest on live-fetched symbols like SPY and QQQ.
analyze_market backtest now reports grouped stats: directional (terminal + per-step accuracy, up/down precision, return correlation), accuracy (terminal MAPE mean/median/p90/worst, path MAPE, MAE/RMSE), bias (signed error, predicted vs actual return), strategy (win rate, profit factor, Sharpe, max drawdown), and naive baselines (buy-and-hold, majority-class direction). fetch_market_data gains an --interval flag for intraday bars (1m/5m/15m/30m/1h) via Alpha Vantage (--av-key / ALPHAVANTAGE_API_KEY), with --months for historical intraday. Daily remains keyless via stockanalysis/nasdaq.
Alpha Vantage moved intraday behind premium. Alpaca's free IEX feed provides 1m/5m/15m/30m/1h bars with generous rate limits via paper-trading keys. The fetcher now paginates via next_page_token for full history depth.
…ng symbols Adds fetch_alpaca_crypto() using the v1beta3 crypto endpoint, with auto-detection of crypto symbols and proper pagination. Output filenames now sanitize / characters so BTC/USD writes to BTCUSD_1h.csv instead of creating subdirectories.
New 'aggregate' mode forecasts N low-timeframe bars then rolls them up into candles at multiple aggregation levels (e.g. 5m -> 15m -> 30m -> 1h -> 2h), scoring each level on candle color (direction), close accuracy, high/low range error, and price-zone overlap (IoU). Tests whether the model reconstructs the coarser-timeframe candle's shape even when individual bars are noisy -- the 'does the paragraph match even if the words differ' framing.
- research/FINDINGS.md: frozen baseline grid, keeper specs, context grade, validated principles, debunked list, goal spec, loop state - engine.py: daily-bar backtest engine (next-open fills, stop/regime/hold support) - 26 study scripts: strategy suite, forensics, intraday scans, options sim, band-walk/trend-break studies, context dictionary, anomaly tests Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A6hZxScXSnjZkT7h87gMe8
…dies Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A6hZxScXSnjZkT7h87gMe8
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A6hZxScXSnjZkT7h87gMe8
…t debunk Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A6hZxScXSnjZkT7h87gMe8
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A6hZxScXSnjZkT7h87gMe8
…alidation Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A6hZxScXSnjZkT7h87gMe8
…nal, not directional
…(downside travels)
…track 2 untested)
…tries lose even in native habitat
…interaction Section 10 in FINDINGS.md. Key findings: - Down-down days recover 57% O→C, but first 30 min determines camp (70% bounce vs 33% flush) - Gap-fill probability scales linearly with magnitude (ladder quantified) - Adding down-down context to IBS doesn't improve it (decayed in 2021-26 half) - Not deployable as trades, but valuable as descriptive market laws Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A6hZxScXSnjZkT7h87gMe8
Entry 75.22 (7/14), exit est 71.23 (7/22 open - 2bp slip). Worst mark -10.2% on 7/20. Two consecutive A-sleeve losses. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A6hZxScXSnjZkT7h87gMe8
TQQQ gapped down from 71.37 close to 69.77 open on exit day. Actual fill 69.77 - 2bp = 69.63, ret = -7.43%. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A6hZxScXSnjZkT7h87gMe8
QQQ closed at 684.23 (IBS 0.17, 5DayLow also fired). Friday + quiet volume give grade 2 despite SMA20<SMA50. Entry est 64.13 (7/27 open +2bp). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A6hZxScXSnjZkT7h87gMe8
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A6hZxScXSnjZkT7h87gMe8
Recreated book_state.json (lost in container recycle). Records 2 closed A-sleeve trades (QQQ -1.04%, TQQQ -7.43%) and 1 open TQQQ position (entry 66.07, 7/27). QQQ IBS=0.867 at 7/30 close triggers exit rule; TQQQ sells at 7/31 open. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A6hZxScXSnjZkT7h87gMe8
QQQ IBS=0.867 at 7/30 close triggered exit. TQQQ sold 7/31 open 65.74 less 2bp = 65.73 vs entry 66.07. Worst mark was -12.6% on 7/29 (TQQQ 57.75) but bounce + gap-up recovered to -0.51%. Book: 3 closed A-sleeve trades, 0 open. All positions flat. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A6hZxScXSnjZkT7h87gMe8
Five-pillar selection (rel vol >=5x, price 2-20, gain >=10pct, float <20M, gap up 2pct), first-pullback entry on crossing candle, stop at pullback low, 2:1 minimum target, 10am hard cutoff. Candidate for new sleeve pending gauntlet: mechanized backtest on historical gappers with realistic small-cap costs. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A6hZxScXSnjZkT7h87gMe8
Pre-registered mechanical spec v1: five-pillar scan (Alpaca screener + FMP float cache), first-pullback state machine on 1-min bars with crossing-candle entry, pullback-low stop, and mechanized exits (topping tail, heavy red, material red, slowdown, 9EMA break, 10am flat). 0.5%/side slippage, 1%-through-stop fills. Runs after close in the daily tracker loop; logs R-multiples to momentum_paper_log.json. Machinery validated on SCKT 8/11 premarket (correct first-pullback DQ). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A6hZxScXSnjZkT7h87gMe8
momentum_paper.py: per-date DST offsets (EDT/EST), injectable bars, raw exit prices for cost-stress recompute, squeeze-resumption fix (a <2-candle dip no longer consumes the first pullback). momentum_backtest.py: 3-stage no-lookahead backtest - full-market daily scan (open-gap >=10%, open $2-20, morning relvol >=5x by 09:45), 1-min replay via the same frozen state machine, stats with percentile distributions, exit-reason table, cost stress, initial-condition quartile cuts, time-half split, concentration check. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A6hZxScXSnjZkT7h87gMe8
…%/side n=99 trades over 1yr. Raw edge +0.163 R/trade at zero slippage decays to -0.105 at 0.5%/side. Lottery-shaped: WR 32%, median R -0.62, max +9.13, top-5 winners carry the sum. Both halves negative at realistic costs. Mechanized topping-tail and material-red exits perform (+0.8R avg). Logged as FINDINGS section 9; forward paper log continues as the clean sample with float pillar applied. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A6hZxScXSnjZkT7h87gMe8
Measures pure price movement after each backtest entry with no exit mechanics: MFE/MAE within the morning window and full day, 10:00 and close checkpoints, and touch-based race between k*R targets and the pullback low. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A6hZxScXSnjZkT7h87gMe8
…momentum scan daily_bars omitted start, so Alpaca returned only the current day's bar and every pillar check saw insufficient history. Now fetches a 200-day window sorted desc and returns chronological bars. First forward scan 2026-08-11: 8 candidates, 3 trades logged (QMCO -0.33R heavy_red, STIM +0.16R heavy_red, GRI -1.14R stop). Floats pending FMP availability. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A6hZxScXSnjZkT7h87gMe8
QMCO 39.0M and STIM 28.3M exceed the 20M float pillar (flagged float_fail); GRI 90K, PLAG 11.6M, WXM 10.0M, AIFA 3.2M, MSGY 341K pass; NIQ 56.2M exceeds. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A6hZxScXSnjZkT7h87gMe8
BOXL closed below VWAP in pullback; QNTU and XHLD retraced >50%. A-sleeve: QQQ IBS 0.180 signal, grade 1 YELLOW (SMA20<SMA50), no entry. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A6hZxScXSnjZkT7h87gMe8
v2 keeps v1 entries and replaces the exit: no stop at the pullback low, take-profit limit ratcheting down as the post-entry dip deepens (20/15/12/8/4 at 0/2/5/10/20% dip), flat 10:00 ET. Schedule was designed on the 2025-08..2026-08 recovery matrix, so that window is in-sample; pass/fail criteria for the 2024-08..2025-08 out-of-sample window are declared in the spec before looking at it. Backtest harness now takes MOMO_START/MOMO_END/MOMO_TAG so the OOS year runs through the identical pipeline. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
v3 keeps v1/v2 entries and sells 25% at each of +5/+10/+20/+35%, moving the stop up to the previously crossed basecamp after each fill. Three pre-registered variants: no initial stop (primary), pullback-low stop, and a dip-conditioned ladder. Same pass/fail battery as v2. Harden backtest networking: retry on timeouts/5xx and checkpoint each scan chunk so a mid-scan failure doesn't discard completed work. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
IS 2025-08..2026-08 (n=98) vs OOS 2024-08..2025-08 (n=71), identical entries, exits varied. - v1 indicator exits replicate exactly OOS: -0.105 R/trade both windows. - v2 (no stop, take-profit ratcheting down with the post-entry dip) is the only scheme meeting all five pre-registered OOS criteria: +3.61%/trade, WR 75%, +2.99% at 1%/side, +2.41% ex-top5, both halves positive. - v3 laddered scale-out fails both windows; the pullback-low variant is the worst scheme tested, a third confirmation that stop placement is the damaging choice here. - Fragility: perturbations keeping dip-conditioning stay positive; removing it collapses to zero. The untuned flat control passed IS and failed OOS. - Survivorship measured by rescanning 1,322 delisted non-OTC symbols over the OOS window: 2 valid entries, both winners under v2. Adds MOMO_UNIVERSE override to the backtest for delisted-universe scans. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Rebuild follows the source notes rather than my tighter rules: every pullback evaluated, intraday qualification, no per-day cap. Candidate pool 1,213 -> 4,792; frequency now 2.3-3.6 trades/week as expected. Full faithful set with v2 exits is ~breakeven (+0.99% OOS, +0.44% IS at 0.5%/side) and negative at 1%/side. First-pullback entries lead in both windows (+5.70%/+1.25%) but robustness disagrees: OOS holds ex-top5 and at 1% costs, IS does not. Percentiles show p75/p90 identical across all buckets (targets cap winners); the entire edge is left-tail avoidance. Runner tranche rejected: selling 75% at target and riding 25% is worse at every trail width tested (20/30/40/50%, flat-10:00, hold-to-close), monotonically worse as the trail widens. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Followed the clue that monsters barely pull back after the target fills. Earlier runner test only went down to a 20% trail and wrongly concluded the runner was dead; tight trails reverse it. Hold to the dip-conditioned level then trail 100% instead of selling: pooled +3.68%/trade at a 2% trail vs +0.65% selling out, with OOS and IS agreeing to within 0.25pt at every width (n=309). Max trade rises from +19.4% to +244%, so this is the mechanism that captures the big movers. Runner fraction is monotone - sell none at the target. Binding constraint is fill quality, not the rule: at 5% slip-through the edge vanishes entirely. A 2-3% trail sits inside the spread on many of these names, which the backtest cannot resolve. Matches the source, which says do not cap winners and exit on the first weakness indicator. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
…lter) Grid search over initial stop x activation x trail width x selectivity, scored on IS with OOS held out. System: pullback width >=8% and price <$8.30; no initial stop; arm at +4%; trail 0.75x ATR(14,1min); flat 10:00 ET. IS n=20 WR 70% +5.00%/trade OOS n=21 WR 71% +10.78%/trade POOL n=41 WR 71% +7.96%/trade, avg win +14.1%, avg loss -6.9% Key results: every fixed initial stop (6-20%) hurts monotonically, so downside control comes from arm-then-trail plus the time exit, which is what keeps the upside uncapped (p10 -1.5%, max +103.8%). Volatility-scaled trailing beats fixed-percentage. Selectivity dominates the exit choice. Double-digit mean per trade is reached pooled at 0% slip-through (+10.10%) and at k=0.5 (+10.44%) but not in both windows independently; recorded as partially achieved rather than claimed. Adds momentum_live.py, verified to reproduce the backtest exactly. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Arming the trailing stop at +2% instead of +4% is the breakthrough. v4's worst trades (-36.3%, -27.8%) were entries that dipped hard, never reached the +4% arm, and rode unprotected to the time exit. Arming at +2% means nearly every entry arms before it can collapse. System: pullback width >=8% and price <$8.30; no initial stop; arm at +2%; trail 0.25x ATR(14,1min); flat 10:00 ET. IS n=20 WR 90% +11.03%/trade OOS n=21 WR 90% +16.75%/trade POOL n=41 WR 90% +13.96%/trade 4 losers of 41, avg loss -1.8%, worst -2.7%; winners avg +15.7%, max +125.6% Upside uncapped, downside limited to scratches, double digits in both windows. Fill sensitivity is the caveat: holds while slip-through stays under ~3%. k=0.25 is deployed rather than the grid optimum (k=0.10) because 0.10 implies a 0.73% stop distance, inside the spread on these names. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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