forked from animatedread/Warrior_EA
e9c562b39f
- Introduced `FeatureScale.mqh` with `FeatSquash` function for stateless feature scaling. - Added `RegimeMath.mqh` class for regime arithmetic, including efficiency and variance calculations. - Documented the Mind trading logic in `MIND.md`, detailing the trading process and modes. - Created `VOLNORM_PLAN.md` and `VOLNORM_RESULTS.md` for tick-volume normalization testing. - Implemented `read_book.py` for analyzing trade book data and correlations. - Developed `volnorm.py` for testing tick-volume normalization with new and old methods.
75 lines
4.5 KiB
Markdown
75 lines
4.5 KiB
Markdown
# The Mind - the trader behind the signal
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`Mind/` turns Warrior_EA from "a signal that fires" into "a signal that fires and a trader who
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looks at it, sizes it, writes it down and reviews the notebook". The validated dip-buy book is
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untouched: the Mind is off the trade path in `MIND_OBSERVE` (the default) and can only take risk
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*off* in the acting modes.
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## Loop
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| step | what | where |
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|---|---|---|
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| LOOK | every modality reads the closed signal bar into one `SMarketContext` | `Modality*.mqh` |
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| RECALL | the reviewer asks how trades in similar conditions have gone | `JournalReviewer.mqh` |
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| SIZE | P(win) -> fraction of base risk, never above 1 (AFML ch. 10) | `BetSizer.mqh` |
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| WRITE | on close, the trade + context + outcome (R, MAE, MFE) go to a CSV and back to the reviewer | `TradeBook.mqh` |
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| ORCHESTRATE | the only class that wires the four | `WarriorMind.mqh` |
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Modalities: **price** (z, ATR-distances, regime, volatility percentile), **volume** (tick volume
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with the hour-of-day removed, and effort-vs-result), **Wyckoff** (phase, last event, spring,
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the reader's verdict), **breadth** (share of the other indices also dipping), **calendar**.
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## Modes (`MindMode`)
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- `MIND_OBSERVE` (default) - trades exactly as without it. Verified 2026-09-30: SP500 2023-01..09,
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same two trades and the same final balance (100,239.27) with the Mind off and observing.
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- `MIND_SIZE` - a setup the journal rates below the book's average gets `m(p)/m(p0)` of the base
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risk (floor `MindMinScale`). Needs `MindMinTrades` closed trades first.
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- `MIND_SIZE_GATE` - as SIZE, and a setup the journal rates a coin flip (`p <= 0.5`) is skipped.
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## Reading the result
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`Common\Files\Warrior_EA\Mind\book_<SYMBOL>_<TF>.csv` - one row per closed trade, 48 columns.
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`python research/read_book.py` pools every book and ranks context fields by rank correlation with R,
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with the multiple-testing count printed under the table. At the end of each run the Experts log
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prints the reviewer's band table, the per-trade Sharpe, PSR(0) and an approximate DSR.
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**How to use it honestly.** A field that looks good on the first backtest is a hypothesis. Run the
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next period (or another index) with it fixed before believing it; `review_p` vs realised R in
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`read_book.py` is the check that the journal's own forecast has any skill.
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## Design rules (why it is shaped like this)
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- **One column contract.** `ENUM_CTX` + `CtxName()` in `MarketContext.mqh`; modalities write it, the
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book persists it, the reviewer buckets it. No other file spells a column.
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- **Open/closed.** A new way of looking = a new `CModality` subclass registered in
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`CWarriorMind::Init()`. Nothing else changes.
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- **Authored rules are priors, not gates.** `CWyckoffReader::Bias()` encodes the Wyckoff books'
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reading, but it only reaches the trade through a journal bucket, so the trades decide whether
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"against the structure" is a worse place to buy a dip. In the first test a dip the reader scored
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-0.75 ("distribution phase E") paid +0.50R - one trade, not a verdict, but the reason for this rule.
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- **Causal.** Modalities read closed bars only; a trade enters the reviewer when it closes.
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In the tester the book is truncated at Init so one run never informs the next.
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## DRY changes made with it
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- `System/RegimeMath.mqh` - the efficiency-ratio / variance-ratio arithmetic that lived inside
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`CWarriorSignal`, now shared with the price modality (signal behaviour unchanged).
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- `Mind/VolumeFeed.mqh` - the one definition of relative volume. `SignalNeural` and the management
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net's crossing features now use it. **This changes their feature vectors** (names renamed so an
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old model file cannot load): retrain, and do not compare against nets trained before 2026-09-30.
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- `System/FeatureScale.mqh` - `FeatSquash`, shared by both.
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- `mql5_patches/` holds older snapshots that no build includes (`FracDiff.mqh` has its own
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`VolumeAt` with the old idea). Left alone; delete or migrate when someone needs them.
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## Known limits
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- The volume level-detrend (`Detrended`) is UNTESTED as a predictor; the book records `rvol_tod`
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(detrended) and `rvol_tod_raw`, so the first journal is its test.
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- The reviewer is additive over four dimensions (volatility, volume, Wyckoff verdict, breadth): it
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cannot see interactions and says so in its header. With ~650 trades in five years there is not
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data for more.
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- Skipped setups (vol-gated dips, vetoes) are not booked, so the Mind cannot yet learn from the
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trades it did not take.
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- The DSR in the run summary uses the large-sample trial variance 1/(T-1); set `MindTrials` to the
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number of configurations really tried.
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