research wave 1: 5 honest tracks on certified BTC/ETH + synthesis
- trackA trend, trackB ML, trackC mean-rev, trackD trend-portfolio, trackE xsec/ensemble - VERDICT: Track D vol-targeted BTC+ETH trend portfolio is the one robust deployable earner (Sharpe 1.0-1.32, DD 13-19%, positive every year 2019-2026) - mean-reversion confirmed dead on clean data; weak-but-real ML/trend residuals - honest: EUR50/day on 2000 in 1-2y is not reachable (needs ~137k capital or ruinous DD)
This commit is contained in:
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# archived data (mirrors top-level data/ ignores, which are top-level-anchored)
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Old/data/
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Old/**/__pycache__/
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.cache_trackE_*.npy
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# 2026-06-19 — Sintesi ricerca post-reset (5 track) e verdetto
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Prima ondata di ricerca sui dati **certificati** BTC/ETH (Deribit mainnet, ~2 bps vs
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Coinbase USD), con harness onesto condiviso `src/backtest/harness.py` (ingresso eseguibile
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a `close[i]`, fee 0.10% RT, exit intrabar TP/SL, OOS/per-anno). Branch
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`strategy-research-2026-06`.
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## I 5 track
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| Track | Famiglia | Esito |
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|-------|----------|-------|
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| **A** | Trend/Momentum (TSMOM, Donchian, EMA, vol-scaled) | 5m/15m morti (fee); 1h = residuo reale ma celle singole non robuste |
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| **B** | ML walk-forward (logistic/GBM su feature di forma) | edge debole ma REALE su BTC (+83% OOS, Sharpe 0.57), ~+0.58 €/d su 2000 |
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| **C** | Mean-reversion / range (fade, RSI2, VWAP) | **MORTO** — negativo anche a fee=0. Conferma: la vecchia libreria fade era artefatto |
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| **D** | **Trend portfolio vol-targeted BTC+ETH** | ✅ **DEPLOYABLE** — robusto, positivo ogni anno |
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| **E** | Cross-sectional BTC↔ETH + ensemble | RV debole (muore a 1.5bps/gamba); ensemble dimezza il DD ma non alza il ritorno |
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## Il vincitore: Track D — trend portfolio (l'unico che guadagna in modo robusto)
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TSMOM multi-orizzonte (blend 1-3-6 mesi su barre 1h), **vol-targeting** (posizione ∝
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1/vol realizzata, target 20% annuo), portafoglio **50/50 BTC+ETH**, fee 0.10% RT. Un solo
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set di parametri per entrambi gli asset.
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- **LONG-SHORT 50/50:** CAGR +14.2%, **Sharpe 1.00**, maxDD 18.9%, positivo ogni anno 2019-2026.
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- **LONG-FLAT 50/50 (migliore risk-adj):** CAGR +15.9%, **Sharpe 1.32**, **maxDD 13.3%**.
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- Robusto: plateau di Sharpe ~1.0 su griglia target-vol/leva/orizzonti; regge fee fino a 0.40% RT;
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su entrambi gli asset; **non** è un picco fortunato (a differenza delle "star" di Track A).
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- Tesi confermata: il valore del trend è **tagliare il drawdown** (B&H DD ~78% → trend DD ~13-19%)
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con Sharpe ≥ B&H → si può scalare il rischio (target-vol) e diversificare BTC+ETH.
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- Caveat onesto: l'edge è più forte 2018-21 (Sharpe 1.63) che 2022-26 (Sharpe 0.57). Dimensionare
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sul regime recente.
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## Il verdetto sul target €50/giorno
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Una strategia che **guadagna** in modo robusto ESISTE (Track D). Ma il target "€50/giorno
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medio partendo da 2000 in 1-2 anni" **non è raggiungibile onestamente**: sono ~2.5%/giorno.
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La leva NON è la scorciatoia (alza il DD verso la rovina). La vera leva è **target-vol +
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capitale + tempo**:
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| target-vol | leva usata | CAGR | Sharpe | maxDD | €/giorno (2k) |
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|-----------|-----------|------|--------|-------|---------------|
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| 20% | 0.23x | +14% | 1.00 | 19% | +0.73 |
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| 40% | 0.45x | +28% | 1.00 | 35% | +3.73 |
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| 60% | 0.68x | +40% | 1.00 | 48% | +7.96 |
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| 80% | 0.90x | +50% | 0.99 | 60% | +13.78 |
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Per **€50/giorno steady-state** servono ~**137k di capitale** (config conservativa, DD~19%),
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oppure DD da rovina. Partendo da 2000 a CAGR ~28% (target-vol 40%, DD 35%) il capitale che
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genera €50/giorno arriva in ~10-13 anni, non in 1-2.
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## Conclusione operativa
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1. **Esiste un edge dispiegabile e onesto**: il trend portfolio vol-targeted (Track D).
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È il primo risultato robusto post-reset.
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2. **Non esiste alcuna scorciatoia** verso €50/giorno su 2000 in 1-2 anni con questi dati
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(BTC/ETH 5m-1h). Il limite è strutturale: due asset, alta correlazione, fee.
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3. Prossimi passi onesti se si vuole alzare il soffitto: (a) dimensionare Track D a un
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target-vol/DD tollerabile e farlo girare in paper, (b) cercare edge di **magnitudine
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diversa** (non più diversificazione di edge deboli) — il che richiede dati che oggi non
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abbiamo certificati (universo più ampio, microstruttura, funding/opzioni backtestabili).
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Script: `scripts/research/track{A,B,C,D,E}_*.py`. Diari di dettaglio: `2026-06-19-track*.md`.
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# 2026-06-19 — Track A: Trend / Momentum su BTC & ETH (dati certificati)
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Prima ricerca di strategie NUOVE post-reset (track A = trend/momentum). Tool:
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`scripts/research/trackA_trend.py` (harness onesto `src/backtest/harness.py`, fee 0.10% RT,
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IS/OOS 65/35, griglia su entrambi gli asset, fee sweep, stress leva). Run:
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`uv run python scripts/research/trackA_trend.py`.
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## Cosa è stato testato
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- **TSMOM** (segno del ritorno N-barre, hold H) long/short e long-only.
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- **EMA crossover** (fast/slow) come filtro di trend.
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- **Donchian breakout** (entry ONESTO: breakout rilevato con `close[i]`, fill a `close[i]`).
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- **Vol-scaled / regime-gated TSMOM** (momentum preso solo se |z| > gate, z = ritorno/vol).
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- Griglia ampia su **BTC e ETH**, **1h / 15m / 5m**. 480 celle OOS totali.
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Tutto entry-eseguibile: direzione e prezzo decisi con dati ≤ `close[i]`, fill a `close[i]`.
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Nessun uso di `returns[i]` (che codifica `close[i+1]`). Hold approssimato come catena di
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posizioni non sovrapposte di H barre (la fee si ammortizza su H barre — costo onesto).
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## Risultati — la fotografia onesta
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**Celle positive OOS per timeframe:**
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| TF | celle positive / totali |
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|----|----|
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| **1h** | 39 / 160 |
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| **15m** | **0 / 160** |
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| **5m** | **0 / 160** |
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→ **Trend intraday (5m/15m) è MORTO**: lo drag della fee (più trade = più 0.10% RT) annienta
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qualsiasi segnale. Drawdown 80-99%, Sharpe da −0.6 a −2.2. Niente da salvare.
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**Su 1h** c'è qualche cella positiva, ma il contesto la ridimensiona:
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- La finestra **OOS è un singolo regime**: il taglio 65% cade a **set/dic 2023**, quindi
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l'OOS è ~2023→2026 (in gran parte toro 2024). Tutto il 2018-2022 (orso 2018, crash 2020,
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toro 2021, orso 2022) è IN-SAMPLE. "Positivo OOS" qui ≈ "il trend ha fatto soldi nel toro 2024".
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- **Benchmark buy & hold sulla stessa finestra OOS**: BTC **+134%**, ETH **−21%**.
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- Tutte le `TSMOM_LONG` e metà delle celle BTC fanno **MENO** del B&H → è **beta**, non edge.
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- Le poche che battono il B&H lo fanno **solo su ETH** (dove il B&H era negativo): catturano
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anche i ribassi. Quello è timing reale — ma vedi sotto.
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**Le "star":** VOLSCALED_TSMOM BTC 1h (N=20,H=48,vw=100,z=0.5) = +367% OOS, Sh 0.91, DD 32%,
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€/d(2k) +2.56; ETH 1h (N=20,H=48,vw=50,z=1.0) = +197% OOS, Sh 0.60. **MA sono celle fortunate:**
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i vicini di griglia crollano (stesso N/H, vw=50 invece di 100 → +21% invece di +367%; z=1.0 → +34%).
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Non è un altopiano robusto, è un picco isolato. E il P&L è concentrato nel 2024 (+110% su BTC),
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con 2025/2026 deboli o negativi per molte celle.
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**Consistenza cross-asset (un edge vero regge su ENTRAMBI):** su 480 celle, solo **2** sono
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positive OOS su BTC *e* ETH:
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- `TSMOM_LONG 1h N=200 H=48` → ma è long-only ≈ beta (fa meno del B&H su BTC).
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- `DONCHIAN 1h N=200 H=12` → l'unico candidato "vero" simmetrico, ma **marginale**:
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OOS BTC +9% / ETH +15%, **Sharpe 0.15-0.19**, troppo debole per dispiegarlo.
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**Fee sweep / leva:** le star reggono lo sweep 0.0005-0.002 (è 1h, poche operazioni), e lo Sharpe
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è invariante alla leva (come deve) — ma la leva 3x porta i DD a 75-91% e affonda le celle marginali.
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## Verdetto
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**Nessun edge trend/momentum dispiegabile, onestamente, su BTC/ETH oggi.**
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- 5m/15m: morti per fee. Chiuso.
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- 1h: esiste un **residuo di segnale trend** (le celle che battono il B&H negativo di ETH non sono
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solo beta), ma è (a) testato su **un solo regime OOS** (toro 2023-2026), (b) **non robusto** di
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griglia (picchi isolati), (c) sull'unica cella simmetrica robusta-su-entrambi (Donchian N=200)
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**troppo debole** (Sharpe ~0.17). Sharpe netti ~0.3-0.9 nel caso migliore = sotto la soglia per
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rischiare capitale reale.
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Conferma la lezione del reset (il superstite storico era trend-following, non mean-reversion): il
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trend è la direzione *meno sbagliata*, ma sui dati certi non basta a fare un edge. Coerente con
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Track C (mean-reversion = artefatto).
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## Prossimi passi possibili (non ancora edge)
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- Walk-forward multi-regime (non un singolo taglio 65/35) per stressare Donchian-1h-N200 su orso 2018/2022.
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- Trend 1h **con filtro di volatilità/regime più ricco** o portafoglio BTC+ETH per diversificare il
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rischio di regime — ma solo se emerge robustezza di griglia, non altri picchi fortunati.
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- Restare scettici: finché un trend non è positivo su griglia + su entrambi gli asset + su ≥2 regimi
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OOS, **non si dispiega**.
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# 2026-06-19 — Track B: ML / feature-prediction su BTC & ETH (walk-forward onesto)
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Esperimento di ricerca sulla direzione **machine-learning** post-reset, su dati Deribit
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mainnet certificati (solo BTC/ETH). Tool: `scripts/research/trackB_ml.py` (runnable
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`uv run python scripts/research/trackB_ml.py`). Tutto netto fee, strict walk-forward,
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held-out tail mai usato per scegliere i config.
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## Metodologia (anti look-ahead — la lezione della v2.0.0)
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- **Feature** (21): ritorni multi-lag (1/2/3/6/12/24), geometria candela (body/upper/lower
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shadow su range, range normalizzato, body lag-1), momentum48 + accelerazione, RSI14,
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estensione ATR-normalizzata vs EMA24, vol realizzata 24/72 + ratio, posizione del close
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nel range 24/72, z-score del volume. **Tutte backward** (note solo a `close[i]`).
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- **Label**: segno del ritorno forward su H barre, `sign(close[i+H]/close[i])`.
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- **Strict walk-forward**: per predire il blocco che inizia a `b`, si addestra
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scaler+modello SOLO su indici `< b-H` (gap di H → label completamente realizzata nel
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passato), finestra rolling delle ultime W barre. Retrain ogni K=250 barre. Mai fit sul
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futuro. **Nessun leakage** (verificato: la label più recente del train usa `close[b-1]`).
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- **Esecuzione**: entry a `close[i]` nella direzione predetta, hold fino a H barre
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(no TP/SL); il no-overlap dell'harness distanzia i trade ≥ H barre.
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- **Modello**: `LogisticRegression(class_weight='balanced')`. Soglia di probabilità per
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filtrare i segnali deboli (long se p>0.5+thr, short se p<0.5-thr, altrimenti flat).
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- **Selezione su DEV** (primo 75%), **conferma una volta sola** sull'held-out tail (ultimo 25%).
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- Griglia: W∈{4000,8000,16000}, H∈{6,12,24,48}, thr∈{0,0.03,0.06,0.10}, BTC & ETH, 1h.
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Fee-sweep 0.05/0.10/0.15/0.20% RT. Turnover/time-in-market sempre riportati.
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## Risultato — esiste un segnale, ma è debole e a basso turnover
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**Pattern netto e robusto della griglia**: la positività compare SOLO nelle celle a basso
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turnover → **W grande (16000) + H lungo (24) + soglia alta (0.10)**. Tutto ciò che gira
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veloce (thr basso, H corto, e soprattutto il **15m**) **muore sulle fee**.
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- **15m**: 0/12 celle positive in dev (la migliore −47%, le altre −99%). Stesso win-rate
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52–56% del 1h, ma il turnover lo polverizza. Conferma di prim'ordine: l'edge per-trade è
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minuscolo, sopravvive solo se si tradano poche barre.
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- **1h, dev**: 19/96 celle net-positive con Sharpe>0. Famiglie threshold-robuste:
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`BTC W16000 H12`, `BTC W8000 H12`, `BTC W16000 H24`, più ETH W16000 H12/H48 marginali.
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### Held-out tail (2024→2026, mai toccato in sviluppo)
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| config | trades | wr% | net% | Sharpe | DD% | mkt% | €/g(2k) | long% | B&H tail |
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|---|---|---|---|---|---|---|---|---|---|
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| **BTC W16000 H24 thr0.10** | 333 | 52.9 | **+83.7** | 0.57 | 23 | 12 | **+0.58** | 44 | +3.9% |
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| BTC W16000 H12 thr0.10 | 382 | 53.4 | +37.6 | 0.35 | 25 | 7 | +0.26 | 54 | +3.9% |
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| ETH W16000 H12 thr0.10 | 364 | 57.7 | +23.7 | 0.24 | 35 | 7 | +0.18 | 68 | −38.4% |
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| ETH W16000 H48 thr0.06 | 215 | 55.3 | −13.3 | 0.08 | 64 | 16 | −0.10 | 67 | −38.4% |
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**Non è solo beta.** Il B&H sul tail è +3.9% (BTC) e −38.4% (ETH), eppure le celle migliori
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fanno +37…+84% (BTC) con **long ~44–54%** (bilanciato long/short), e ETH +23.7% **mentre ETH
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scendeva −38%** (short corretti). Quindi c'è segnale direzionale genuino, non cattura di trend
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rialzista. Payoff asimmetrico: ~53% WR ma avgWin>avgLoss (BTC: +2.04% vs −1.63%).
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### Fee-sweep (held-out)
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- `BTC W16000 H12 thr0.10`: 0.05%→+66.6 | **0.10%→+37.6** | 0.15%→+13.7 | 0.20%→−6.1.
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Sopravvive fino a ~0.15% RT, poi muore. Margine sottile.
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- `BTC W8000 H12 thr0.06`: positivo solo a 0.05%, già −35% a 0.10%. Fragile.
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- ETH e le celle a turnover medio: muoiono tra 0.10 e 0.15%.
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### Stabilità per-anno (full walk-forward, BTC W16000 H24 thr0.10)
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`+11% (2020) / +188% (2021) / +14% (2022) / −38% (2023) / +13% (2024) / +75% (2025) / +7% (2026)`,
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CAGR full ~22%, ma **DD 56%** e fortissima concentrazione su 2021/2025 con un 2023 a −38%.
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## Verdetto onesto — NON deployabile verso l'obiettivo
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1. **L'edge è reale ma minuscolo.** A differenza della vecchia libreria (artefatto puro), qui
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il segnale sopravvive a strict walk-forward, a fee 0.10% RT e batte il B&H sul tail. È un
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risultato genuino e va registrato: la direzione ML **non è morta**.
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2. **Ma è incompatibile col target.** €/giorno su €2000 = +0.26…+0.58 baseline (anche la stima
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rosea full-WF CAGR 22% → ~€1–3/g). Il target è **€50/g** → siamo ~100x sotto.
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3. **Fragilità**: vive solo a basso turnover (thr alto, H lungo, W grande), DD 23–56%,
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ritorni concentrati in pochi anni con un anno a −38%, e l'edge si assottiglia già a
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0.15% RT. Un singolo cambio di regime lo annulla.
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4. **ETH ≠ "specialmente buono"** (contrariamente all'indizio dello shape-ML precedente): qui
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ETH è più sottile e più rumoroso di BTC sull'held-out; l'unico merito è aver shortato
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correttamente il drawdown 2024-25.
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**Conclusione**: la logistic-regression walk-forward su feature di forma+momentum trova un
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debole edge **momentum direzionale a basso turnover** su BTC (più tenue su ETH), onesto e
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netto-fee, ma **troppo piccolo, troppo concentrato e troppo fee-sensibile** per essere
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deployato standalone. Al massimo un **componente** di un futuro ensemble, e solo nelle
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configurazioni a bassissimo turnover. Nessun config raggiunge, neanche lontanamente, i €50/g.
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## Prossimi passi possibili (non eseguiti)
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- Provare **predizione di magnitudine/asimmetria** (large-up vs large-down) e position-sizing
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proporzionale alla confidenza, invece del semplice segno.
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- **GradientBoosting / feature non lineari** (flag `--gbm` predisposto) — ma attenzione
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all'overfit; il rischio è di "trovare" edge che il walk-forward onesto non conferma.
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- **Ensemble** del segnale ML a basso turnover con un filtro di regime (vol/trend) per tagliare
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il 2023. Ma serve dimostrare che il filtro non è scelto col senno di poi.
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- Restare scettici: finché €/g resta ~100x sotto target, l'ML da solo NON è la risposta.
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@@ -0,0 +1,70 @@
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# 2026-06-19 — Track C: mean-reversion / range re-examination (HONEST) → DEAD
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Obiettivo: stabilire rigorosamente se **un qualunque** edge di mean-reversion / range a
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breve orizzonte sopravvive su BTC/ETH **certificati** (Deribit mainnet) con **ingresso
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eseguibile onesto**, oppure confermarne definitivamente la morte. Entrambi gli esiti sono
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validi; nessun risultato forzato.
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Tool: `scripts/research/trackC_meanrev.py` (self-contained, runnable), sopra l'harness
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onesto `src/backtest/harness.py` (direzione+prezzo decisi con dati ≤ `close[i]`, fill a
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`close[i]`, exit intrabar TP/SL da `i+1`, fee netto). Universo: BTC/ETH × {1h,15m,5m}.
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## Cosa è stato testato (5 famiglie, ingresso onesto)
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- **ZFADE** — Bollinger/z-score fade: `z(close,lookback)` ≤ −thr → long, ≥ +thr → short.
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TP al mean mobile o a `tp_atr·ATR`; SL a `sl_atr·ATR`. **Entry a close[i]**, NON al tocco
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della banda (era proprio quello l'artefatto storico).
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- **RSI2** — RSI(2) oversold/overbought (+ variante con filtro trend SMA200).
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- **RETREV** — return reversal: fade del rendimento cumulato estremo (|z| > thr·σ).
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- **VWAP** — reversione sulla distanza dal VWAP rolling (in unità di σ della distanza).
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- **SESSION** — autocorrelazione next-bar per ora UTC (descrittivo).
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||||
|
||||
Metodologia applicata: OOS 65/35, griglia parametri su **entrambi** gli asset, fee-sweep
|
||||
{0, 0.5, 1.0, 1.5, 2.0} bps RT, cross-check liquidità (flat-bar O=H=L=C) e time-in-market.
|
||||
|
||||
## Sanity liquidità
|
||||
|
||||
Flat-bar O=H=L=C: BTC/ETH 1h ≈ 0.01%, 15m 0.09–0.14%. Book vivo → l'eventuale edge NON
|
||||
potrebbe nascondersi in barre ferme (a differenza degli alt archiviati). Confermato pulito.
|
||||
|
||||
## Risultati — tutto negativo, su ogni asse
|
||||
|
||||
**PASS 1 (screen 1h, fee 0.10% RT):** ogni famiglia OOS negativa su entrambi gli asset.
|
||||
Es. ZFADE z2/mean: BTC OOS −85%, ETH OOS −83%. RSI2 10/90: BTC −92%, ETH −96%.
|
||||
RETREV/VWAP idem. Win-rate spesso "alto" (RSI2 ~63%, VWAP ~64%) ma **perde lo stesso** →
|
||||
le poche perdite sono enormi, la reversione non paga il rischio + fee.
|
||||
|
||||
**PASS 2 (griglia 1h):** ZFADE **0/18** celle con OOS>0 su entrambi; RSI2 **0/36**. La
|
||||
cella meno-peggio (ZFADE lookback20 z3) resta BTC −40% / ETH −33% OOS. Nessun sopravvissuto.
|
||||
|
||||
**PASS 3 (fee-sweep, incl. fee=0 GROSS):** il colpo decisivo. **Anche a fee=0** (lordo)
|
||||
la z-fade è negativa: BTC full −74% / OOS −46%, ETH full −98% / OOS −48%. Quindi non è
|
||||
"morte da fee": **la direzione stessa della fade è sbagliata** sul feed pulito. Salendo le
|
||||
fee degrada monotòno fino a −100%.
|
||||
|
||||
**PASS 4 (timeframe 5m/15m/1h):** più veloce = peggio. A 5m full −100% su entrambi
|
||||
(41.889 / 38.660 trade), €/giorno su 2000 ≈ −0.70/−0.75. Coerente con "molte operazioni =
|
||||
morte per fee", ma il PASS 3 mostra che il problema è a monte: niente edge nemmeno lordo.
|
||||
|
||||
**PASS 5 (sessione UTC):** esiste una **debole** autocorrelazione negativa next-bar in
|
||||
poche ore (BTC 13h −0.166, 2h −0.154, 21h −0.129; ETH 13h −0.152, 4h −0.117), e una
|
||||
positiva alle 03h UTC (BTC +0.158, ETH +0.202 = ora "trending"). Struttura reale ma
|
||||
debolissima (|ρ|≤0.17): non sopravvive a fee + dimensionamento del rischio (lo conferma il
|
||||
fatto che tutte le versioni *tradate* perdono anche lorde).
|
||||
|
||||
## Verdetto
|
||||
|
||||
**Nessuna** configurazione MR produce OOS netto>0 su entrambi BTC ed ETH a fee baseline.
|
||||
Più forte: **a fee zero la fade è già negativa** → l'edge MR storico (+201%/+1238% "OOS")
|
||||
era un **artefatto del feed contaminato** (wick fantasma testnet + entry su estremi mai
|
||||
scambiati), non una proprietà del mercato. Sul dato certificato, con ingresso eseguibile,
|
||||
la mean-reversion a breve orizzonte **non è un edge**: è morta sia lorda che netta.
|
||||
|
||||
Coerente con la tesi del reset (`2026-06-19-deribit-history.md`, §3): FADE morto ogni anno.
|
||||
Track C chiusa come direzione di alpha. La debole struttura intraday-by-hour (PASS 5) è
|
||||
annotata ma non azionabile da sola; semmai un *filtro* futuro, non una strategia.
|
||||
|
||||
## Artefatti
|
||||
|
||||
- `scripts/research/trackC_meanrev.py` — riproducibile: `uv run python
|
||||
scripts/research/trackC_meanrev.py [--quick]` (~40s quick, ~3min full).
|
||||
@@ -0,0 +1,96 @@
|
||||
# 2026-06-19 — Track D: Robust walk-forward TREND PORTFOLIO (BTC+ETH), vol-targeted + leverage
|
||||
|
||||
Follow-up to Track A. Thesis under test: trend-following's real value in crypto is **drawdown
|
||||
reduction** vs buy & hold (it sidesteps crashes), and that lower DD lets you apply **leverage** and
|
||||
**diversify** BTC+ETH into a deployable, risk-adjusted *earning* system — even if each single signal
|
||||
has modest Sharpe. Tool: `scripts/research/trackD_trendport.py` (run
|
||||
`uv run python scripts/research/trackD_trendport.py`).
|
||||
|
||||
## Method (honest, no look-ahead)
|
||||
Equity built directly from a **target-position series** (the harness's documented "build your own
|
||||
equity" path), NOT per-trade chaining:
|
||||
- `target[i]` decided with data **≤ close[i]**; **held during the next bar** (close[i]→close[i+1]).
|
||||
- `pnl[t] = target[t-1]·r[t]`, `r[t]=close[t]/close[t-1]-1` — positions **shifted +1 bar** ⇒ no leakage.
|
||||
- Fees on **turnover**: `0.05%/side·|target[t-1]-target[t-2]|` (0.10% RT baseline; swept 0.10–0.40% RT).
|
||||
- **Vol-targeting** (main lever): `target = direction · (target_vol / realized_vol)`, clipped to the
|
||||
leverage cap. `realized_vol` = annualized rolling std of past bar returns (30d window), ≤ close[i].
|
||||
- **Portfolio** = 50/50 BTC+ETH net-return series, rebalanced each bar on common timestamps.
|
||||
|
||||
Leakage sanity check passed: an *oracle* target using next-bar sign explodes (10^119×) — proving the
|
||||
engine holds `target[i-1]` over bar `i` — while our signals (TSMOM blend, MA-slope, Donchian) only use
|
||||
`close[i]` and earlier. Zero-position equity = exactly 1.0.
|
||||
|
||||
## What was tested
|
||||
TSMOM multi-horizon blend (1/3/6-month-equiv on 1h bars), MA-slope (EMA200 slope), Donchian breakout
|
||||
with trailing channel stop — each vol-targeted, long-short **and** long-flat, per-asset and combined.
|
||||
Grid: target-vol × leverage-cap × horizon-set; explicit EARLY(2018-21)/LATE(2022-26) split;
|
||||
fee & leverage sweep; full per-year 2018-2026.
|
||||
|
||||
## Results — the honest picture
|
||||
|
||||
**1) The thesis holds: massive DD reduction, and diversification helps.**
|
||||
| Strategy (50/50 port, tvol20%, LS) | CAGR | Sharpe | maxDD | volA |
|
||||
|---|---|---|---|---|
|
||||
| **B&H 50/50** | +48% | 0.92 | **77.8%** | 70% |
|
||||
| TSMOM 1-3-6m blend | +14.2% | **1.00** | **18.9%** | 14% |
|
||||
| MA-slope | +14.1% | 0.79 | 21.9% | 19% |
|
||||
| Donchian-trailing | +14.7% | 0.89 | 17.7% | 17% |
|
||||
|
||||
Trend cuts maxDD from ~78% to ~18% while keeping a Sharpe **above** buy&hold (1.00 vs 0.92). The
|
||||
portfolio Sharpe (1.00) **beats both sleeves** (BTC 0.95, ETH 0.75) — diversification works as claimed.
|
||||
The **long-flat** variant is even cleaner: Sharpe **1.32**, maxDD **13.3%** (no short funding/borrow risk).
|
||||
|
||||
**2) It is genuinely robust (not a lucky cell).**
|
||||
- *Per-year (headline LS):* every full year **positive** 2019-2025 (+19/+36/+19/+6/+2/+14/+4%) and 2026 +8%.
|
||||
- *Grid:* Sharpe ≈1.00 across **all** target-vol (10-40%) × leverage caps — flat plateau (vol-targeting
|
||||
just scales). DD scales ~linearly with target-vol (10%→DD10%, 40%→DD35%).
|
||||
- *Horizon-set:* every subset (1m/3m/6m/1-3m/3-6m/1-2-4m/2-4-8m) is **positive**; Sharpe 0.37→1.39.
|
||||
Shorter horizons (1m, 1-2-4m) score best (Sharpe 1.34-1.39) — a real plateau, not one combo.
|
||||
- *Fee:* survives to 0.40% RT (Sharpe 1.00→0.39, still positive at 4× baseline fee).
|
||||
|
||||
**3) The honest caveat — most of the edge is the EARLY regime.**
|
||||
Walk-forward split, same param set both assets:
|
||||
- **EARLY 2018-2021:** CAGR +26%, Sharpe **1.63**, DD 18%.
|
||||
- **LATE 2022-2026:** CAGR +7.3%, Sharpe **0.57**, DD 19%.
|
||||
The signal is real and still net-positive every late year, but its quality **halved** post-2021
|
||||
(crypto vol compressed, trends choppier). This is the same warning Track A raised, now quantified: the
|
||||
edge is strongest 2019-2021 and merely *modest* in the 2022-26 regime.
|
||||
|
||||
**4) Leverage is a red herring; target-vol is the real dial — and it costs DD linearly.**
|
||||
At tvol=20% on 60-80% crypto vol, positions stay **sub-1x** (avg gross 0.23×): the leverage cap
|
||||
**never binds**. To deploy real leverage you raise target-vol; Sharpe stays ~1.0, DD scales:
|
||||
|
||||
| target_vol | avg gross | CAGR | Sharpe | maxDD |
|
||||
|---|---|---|---|---|
|
||||
| 20% | 0.23× | +14% | 1.00 | 19% |
|
||||
| 40% | 0.45× | +28% | 1.00 | 35% |
|
||||
| 60% | 0.68× | +40% | 1.00 | 48% |
|
||||
| 80% | 0.90× | +50% | 1.00 | 60% |
|
||||
| 100% | 1.12× | +58% | 0.99 | 69% |
|
||||
|
||||
## Verdict — is this a deployable earning system?
|
||||
|
||||
**Yes as a risk-adjusted system; NO as a fast path to €50/day on €2000.**
|
||||
|
||||
- This is the **first post-reset config that is genuinely robust**: Sharpe ~1.0 (long-flat 1.3),
|
||||
positive every year 2018-2026, robust across grid/horizon/fee, on both assets, on certified data,
|
||||
with honest no-look-ahead accounting. It is a real, deployable trend portfolio and a clear
|
||||
improvement over Track A's lucky single cells. The thesis (DD reduction → leverageable, diversifiable)
|
||||
is **confirmed**.
|
||||
- **But the earnings are modest.** Headline (tvol20%, 2x cap, LS): CAGR **+14.2%**, DD 19% ⇒ steady-state
|
||||
**~€0.73/day on €2000**. To average **€50/day at this CAGR you need ~€137k capital**, not €2000.
|
||||
- **Leverage can't close the gap cheaply.** Pushing target-vol to 80% gives CAGR ~50% (DD **60%**) — and
|
||||
at €2000, 50%/yr is still only ~€2.7/day in steady state. Reaching €50/day in 1-2 years from €2000
|
||||
would require both heavy leverage (DD 60-70%, near-ruin) **and** lucky path — not a sane plan.
|
||||
- **Regime risk:** the edge is much weaker post-2021 (Sharpe 0.57 LATE). Deploy sized for the LATE
|
||||
regime, not the EARLY one.
|
||||
|
||||
**Recommendation:** treat this as the **core risk engine** (compounding ~14%/yr at DD<20%, or
|
||||
long-flat ~16%/yr at DD 13%), deployable now at low size to validate live execution. It grows €2000,
|
||||
but to *€50/day* the lever is **capital + time**, not leverage. Realistic near-term: ~€0.7-1.5/day on
|
||||
€2000; €50/day needs ~€70-140k or a second uncorrelated edge stacked on top.
|
||||
|
||||
## Deliverable
|
||||
`scripts/research/trackD_trendport.py` — self-contained, prints B&H benchmark, broad scan, grid
|
||||
robustness, horizon robustness, walk-forward early/late, fee+leverage sweep, headline config per-year,
|
||||
and the path-to-€50/day table. Reusable building blocks (vol-targeting, target→equity, portfolio).
|
||||
@@ -0,0 +1,140 @@
|
||||
# 2026-06-19 — Track E: Cross-sectional BTC↔ETH relative-value + ENSEMBLE synthesis
|
||||
|
||||
Due parti, entrambe oneste e su dati Deribit-mainnet certificati (solo BTC/ETH). Tool:
|
||||
`scripts/research/trackE_xsec_ensemble.py` (runnable, self-contained, riusa il walk-forward
|
||||
ML di Track B e il Donchian di Track A). Harness onesto: direzione/posizione decise con dati
|
||||
≤ `close[i]`, realizzo sul bar successivo (shift di 1 barra, niente look-ahead). Fee
|
||||
turnover-based: `|Δpos|·fee_rt/2` **per gamba** (un flip +1↔−1 = un round-trip = 0.10% RT).
|
||||
|
||||
Run: `uv run python scripts/research/trackE_xsec_ensemble.py` (`--quick` salta lo sleeve ML;
|
||||
`--no-cache` ricalcola la proba ML). Il proba ML viene cacheato (`.cache_trackE_*.npy`).
|
||||
|
||||
---
|
||||
|
||||
## PART 1 — Relative value (spread BTC↔ETH, 1h, market-neutral)
|
||||
|
||||
**Premessa strutturale.** BTC/ETH log-ret 1h sono correlati **0.84**. Con due soli asset
|
||||
l'unica struttura tradabile è lo **spread**. E con due asset, *"long il più forte / short il
|
||||
più debole"* (XS-momentum) è **algebraicamente identico** a *"trada il trend del ratio
|
||||
ETH/BTC"* — infatti nel codice (A) e (B) producono numeri identici. Sono lo stesso edge.
|
||||
|
||||
**Lead-lag: nullo.** `corr(rB[i], rE[i+1]) = −0.018`, `corr(rE[i], rB[i+1]) = −0.007`,
|
||||
autocorrelazioni −0.01..−0.02. Nessun potere predittivo cross-asset → lead-lag **non**
|
||||
perseguito come sleeve (sarebbe rumore moltiplicato per le fee).
|
||||
|
||||
**(A/B) XS momentum / ratio trend (griglia N∈{24,72,168,336}, hold∈{6,24,72}):**
|
||||
- Solo **4/12 celle** OOS net-positive, e sparse (N24/h24, N24/h72, N72/h72, N168/h24).
|
||||
- Le celle FULL forti (N168/h24: +150% full, Sharpe 0.68, DD 27%) hanno **OOS debole**
|
||||
(+11%, Sh 0.30). La migliore per OOS-Sharpe è N24/h24 (OOS Sh 0.31, OOS net +11%).
|
||||
- **Fee sweep (N24/h24):** gross (0bp) FULL +356%/OOS +74% Sh 1.20 → a 1.0bp/gamba FULL +27%/
|
||||
OOS +11% Sh 0.31 → **muore già a 1.5bp/gamba** (OOS −11%). Margine fee sottilissimo.
|
||||
- **Per-anno** concentrato sui grandi movimenti del ratio 2020-2021 (e 2024), piatto/negativo
|
||||
altrove (2022 −9%, 2023 −19%, 2025 −6%, 2026 −16%). Non è un altopiano: è un edge debole,
|
||||
fee-sensibile, regime-dipendente.
|
||||
|
||||
**(C) Ratio mean-reversion (z-fade di log(ETH/BTC)):** negativa ovunque (es. lb168/zin2.0:
|
||||
FULL −85%, OOS −44%, Sh −1.56). Coerente con Track C: anche sullo spread la MR a breve non è
|
||||
un edge sul dato pulito.
|
||||
|
||||
**Verdetto PART 1:** esiste un **debole** edge di relative-value (XS-momentum ≡ ratio-trend),
|
||||
net-positivo OOS solo in alcune celle, Sharpe OOS ~0.3, che **muore a ~1.5bp/gamba** ed è
|
||||
concentrato in pochi anni. È **reale ma marginale** — degno di entrare in un ensemble come
|
||||
sleeve diversificante, non come strategia standalone. La sua virtù: è **quasi scorrelato**
|
||||
dagli edge direzionali (vedi sotto).
|
||||
|
||||
---
|
||||
|
||||
## PART 2 — Ensemble (3 sleeve residui in UN portafoglio)
|
||||
|
||||
Sleeve combinati (gross 1 ciascuno, equal-weight 1/N → gross totale ~1):
|
||||
- **S1 = BTC-ML** (Track B, cella onesta a basso turnover W16000 H24 thr0.10, 1h).
|
||||
- **S2 = BTC-Trend** (Track A, l'unica cella trend robusta cross-asset: Donchian N=200 H=12).
|
||||
- **S3 = Relative-value** (PART 1, miglior cella OOS: XS-momentum N=24 hold=24).
|
||||
|
||||
**Finestra comune attiva** (dove tutti e 3 sono live, dopo il warmup ML): 2020-06 → 2026-06,
|
||||
52.636 barre.
|
||||
|
||||
### Matrice di correlazione degli sleeve (ret per-barra, finestra comune)
|
||||
|
||||
| | S2_trend | S3_relval | S1_ml |
|
||||
|----------|----------|-----------|--------|
|
||||
| S2_trend | +1.000 | +0.010 | −0.063 |
|
||||
| S3_relval| +0.010 | +1.000 | −0.010 |
|
||||
| S1_ml | −0.063 | −0.010 | +1.000 |
|
||||
|
||||
→ **Sleeve quasi perfettamente scorrelati** (|ρ| ≤ 0.06). In teoria, terreno ideale per la
|
||||
diversificazione.
|
||||
|
||||
### Per-sleeve (finestra comune, scala $ uguale)
|
||||
|
||||
| sleeve | net | Sharpe | maxDD | €/g(2k) |
|
||||
|-----------|-------|--------|-------|---------|
|
||||
| S2_trend | +5% | +0.15 | 34% | +0.04 |
|
||||
| S3_relval | +8% | +0.16 | 41% | +0.07 |
|
||||
| **S1_ml** | +382% | **+0.87** | 56% | +3.51 |
|
||||
|
||||
### Ensemble
|
||||
|
||||
| portafoglio | net | Sharpe | maxDD | CAGR | €/g(2k) |
|
||||
|----------------------|-------|--------|-------|-------|---------|
|
||||
| best single (S1_ml) | +382% | +0.87 | 56% | +30% | +3.51 |
|
||||
| **EQUAL-WEIGHT 1/N** | +109% | **+0.83** | **30%** | +13% | +1.00 |
|
||||
| inverse-vol (IS wts) | +76% | +0.70 | 29% | +10% | +0.69 |
|
||||
| EQ-WEIGHT **OOS**(65/35)| +32% | **+1.02** | **12%** | +14% | +0.83 |
|
||||
|
||||
Per-anno equal-weight: 2020 +16%, 2021 +50%, 2022 +2%, **2023 −13%** (vs −38% dell'ML da
|
||||
solo!), 2024 +18%, 2025 +19%, 2026 −3%. **Molto più liscio**, niente anno-catastrofe.
|
||||
|
||||
### La diversificazione aiuta? Sì sul rischio, NO sul rendimento risk-adjusted
|
||||
|
||||
- **Sharpe:** ensemble 0.83 vs best-single 0.87 → **non batte** il miglior sleeve singolo.
|
||||
- **maxDD:** ensemble **30%** vs best-single 56% → **dimezzato**. E OOS 12% vs ML-solo molto
|
||||
più profondo. Per-anno senza il −38% del 2023.
|
||||
- **Risk-matched** (levare l'ensemble 1.84x per pareggiare il 56% DD dell'ML): €/g +2.23
|
||||
contro €/g +3.51 dell'ML da solo → a pari drawdown l'ensemble rende **MENO** (ratio 0.64).
|
||||
|
||||
**Perché?** Gli sleeve sono scorrelati ma **enormemente diseguali** (Sharpe 0.87 vs 0.15 vs
|
||||
0.16). L'equal-weight 1/N "annacqua" l'unico sleeve forte con due deboli: la matematica
|
||||
della diversificazione alza lo Sharpe solo se gli sleeve sono di *qualità comparabile*. Qui
|
||||
non lo sono, quindi 1/N non può superare il singolo migliore. Pesare verso l'ML (quality-
|
||||
weighting) converge banalmente a "esegui solo l'ML" — e sarebbe in-sample.
|
||||
|
||||
**Il guadagno vero dell'ensemble è la ROBUSTEZZA, non il rendimento:** stesso Sharpe del
|
||||
miglior sleeve a **metà del drawdown**, per-anno molto più stabile, niente dipendenza da un
|
||||
singolo modello/regime (l'ML da solo concentra tutto in 2021/2025 con un −38% nel 2023). Per
|
||||
chi deve *sopravvivere*, l'ensemble è preferibile; per chi massimizza il rendimento a pari
|
||||
rischio, l'ML puro vince di un soffio.
|
||||
|
||||
---
|
||||
|
||||
## Verdetto onesto — è un motore da €50/giorno? NO.
|
||||
|
||||
1. **Relative-value:** edge debole, reale ma marginale (Sharpe OOS ~0.3), fee-sensibile
|
||||
(muore a 1.5bp/gamba), concentrato 2020-2021/2024. Utile **solo** come sleeve scorrelato.
|
||||
Lead-lag e ratio-MR: nulli/negativi.
|
||||
2. **Ensemble:** gli sleeve sono **quasi scorrelati** (|ρ|≤0.06) — risultato genuino e bello.
|
||||
L'ensemble equal-weight ottiene **Sharpe ~0.83 a metà del drawdown** del miglior sleeve e
|
||||
un per-anno molto più liscio. **Ma NON alza il tetto risk-adjusted** (a pari DD rende meno
|
||||
dell'ML puro) perché un solo sleeve domina.
|
||||
3. **Distanza dal target:** ensemble **€1.00/giorno su €2000** (best single €3.51 ma a DD
|
||||
56% e concentrato). Il target è **€50/giorno → ~50x sotto** (l'ML puro ~14x sotto ma con
|
||||
rischio/concentrazione inaccettabili). Levare per colmare il gap moltiplica il drawdown
|
||||
ben oltre il tollerabile (1.84x già porta al 51% DD per ~€2.2/g).
|
||||
|
||||
**Conclusione:** la sintesi di Track E conferma la fotografia dei track A/B/C — esistono
|
||||
**edge residui deboli ma reali e scorrelati** su BTC/ETH. Combinarli in un ensemble **migliora
|
||||
la robustezza** (DD dimezzato, per-anno stabile, niente single-point-of-failure) ma **non crea
|
||||
rendimento dal nulla**: il sistema combinato rende ~€1/giorno su €2000, ~50x sotto l'obiettivo,
|
||||
e non è un motore dispiegabile. Il miglior uso pratico dei risultati: se un giorno si tradasse,
|
||||
l'ensemble equal-weight (ML + trend + relative-value) è la forma **più onesta e meno fragile**
|
||||
del poco edge disponibile — ma serve un edge **di un'altra magnitudine** per avvicinare i €50/g.
|
||||
|
||||
## Prossimi passi possibili (non eseguiti)
|
||||
- Cercare uno sleeve **di qualità comparabile all'ML** (Sharpe ≥0.5 indipendente) — solo
|
||||
allora 1/N alzerebbe lo Sharpe oltre il singolo. Senza, l'ensemble resta solo "risk smoother".
|
||||
- Relative-value su **timeframe diversi** del ratio (giornaliero?) o con **position sizing**
|
||||
proporzionale alla forza del segnale, restando scettici sul fee-margin sottile.
|
||||
- Non aumentare la leva per inseguire €50/g: il DD esplode prima del rendimento.
|
||||
|
||||
## Artefatti
|
||||
- `scripts/research/trackE_xsec_ensemble.py` — riproducibile (`uv run ...`, ~8s con cache ML).
|
||||
@@ -0,0 +1,320 @@
|
||||
"""TRACK A — TREND / MOMENTUM research on certified BTC/ETH (Deribit mainnet).
|
||||
|
||||
Honest harness only (src.backtest.harness). Rules enforced:
|
||||
* Direction & entry price decided with data <= close[i]; fill at close[i].
|
||||
* Net of fees (0.10% RT baseline) + fee sweep + leverage stress.
|
||||
* IS / OOS split (65/35). Grid robustness across params AND both assets.
|
||||
|
||||
Run: uv run python scripts/research/trackA_trend.py
|
||||
|
||||
This script is deliberately skeptical: it prints full grids so the reader can see
|
||||
whether an "edge" is a single lucky cell or a robust neighborhood. The verdict at the
|
||||
end is printed from the actual numbers, not asserted.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
|
||||
|
||||
from src.backtest.harness import load, backtest_signals, oos_split
|
||||
|
||||
ASSETS = ["BTC", "ETH"]
|
||||
TFS = ["1h", "15m", "5m"]
|
||||
FEE = 0.001
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Signal builders. Each returns a list[dict|None] of length len(df).
|
||||
# All features use ONLY data up to and including close[i]. Entry fills at close[i].
|
||||
# Position is approximated as a chained, non-overlapping hold of `hold` bars whose
|
||||
# direction is recomputed at each (free) bar -> amortizes fee over `hold` bars while
|
||||
# staying honest about responsiveness.
|
||||
# ---------------------------------------------------------------------------
|
||||
def sig_tsmom(df, lookback, hold, long_only=False):
|
||||
c = df["close"].values
|
||||
n = len(c)
|
||||
ent = [None] * n
|
||||
dirs = np.where(c[lookback:] > c[:-lookback], 1, -1)
|
||||
for k, d in enumerate(dirs):
|
||||
if long_only and d < 0:
|
||||
continue
|
||||
ent[lookback + k] = {"dir": int(d), "max_bars": hold}
|
||||
return ent
|
||||
|
||||
|
||||
def _ema(x, span):
|
||||
return pd.Series(x).ewm(span=span, adjust=False).mean().values
|
||||
|
||||
|
||||
def sig_ema_cross(df, fast, slow, hold, long_only=False):
|
||||
c = df["close"].values
|
||||
n = len(c)
|
||||
ef = _ema(c, fast)
|
||||
es = _ema(c, slow)
|
||||
ent = [None] * n
|
||||
for i in range(slow, n):
|
||||
d = 1 if ef[i] > es[i] else -1
|
||||
if long_only and d < 0:
|
||||
ent[i] = None
|
||||
continue
|
||||
ent[i] = {"dir": d, "max_bars": hold}
|
||||
return ent
|
||||
|
||||
|
||||
def sig_donchian(df, lookback, hold, long_only=False):
|
||||
"""Breakout: close[i] strictly above prior `lookback` highs -> long; below lows -> short.
|
||||
Detection AND entry both at close[i] (honest)."""
|
||||
c = df["close"].values
|
||||
h = df["high"].values
|
||||
l = df["low"].values
|
||||
n = len(c)
|
||||
ent = [None] * n
|
||||
# prior-window high/low EXCLUDING current bar (shift by 1) -> honest
|
||||
hh = pd.Series(h).rolling(lookback).max().shift(1).values
|
||||
ll = pd.Series(l).rolling(lookback).min().shift(1).values
|
||||
for i in range(lookback, n):
|
||||
if not np.isfinite(hh[i]):
|
||||
continue
|
||||
if c[i] > hh[i]:
|
||||
d = 1
|
||||
elif c[i] < ll[i]:
|
||||
d = -1
|
||||
else:
|
||||
continue
|
||||
if long_only and d < 0:
|
||||
continue
|
||||
ent[i] = {"dir": d, "max_bars": hold}
|
||||
return ent
|
||||
|
||||
|
||||
def sig_vol_scaled_tsmom(df, lookback, hold, vol_win, z_gate):
|
||||
"""Momentum gated by trend strength: only take a position when |past return| exceeds
|
||||
z_gate * rolling stdev of bar returns (regime gate). Honest: all <= close[i]."""
|
||||
c = df["close"].values
|
||||
n = len(c)
|
||||
logret = np.zeros(n)
|
||||
logret[1:] = np.diff(np.log(c))
|
||||
vol = pd.Series(logret).rolling(vol_win).std().values
|
||||
ent = [None] * n
|
||||
start = max(lookback, vol_win) + 1
|
||||
for i in range(start, n):
|
||||
r = np.log(c[i] / c[i - lookback])
|
||||
v = vol[i] * np.sqrt(lookback)
|
||||
if not np.isfinite(v) or v == 0:
|
||||
continue
|
||||
z = r / v
|
||||
if abs(z) < z_gate:
|
||||
continue
|
||||
d = 1 if z > 0 else -1
|
||||
ent[i] = {"dir": d, "max_bars": hold}
|
||||
return ent
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Evaluation helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
def eval_is_oos(df, entries, asset, tf, fee=FEE, lev=1.0):
|
||||
cut = oos_split(df, 0.65)
|
||||
full = backtest_signals(df, entries, fee_rt=fee, leverage=lev, asset=asset, tf=tf)
|
||||
ent_is = [e if i < cut else None for i, e in enumerate(entries)]
|
||||
ent_oos = [e if i >= cut else None for i, e in enumerate(entries)]
|
||||
m_is = backtest_signals(df, ent_is, fee_rt=fee, leverage=lev, asset=asset, tf=tf)
|
||||
m_oos = backtest_signals(df, ent_oos, fee_rt=fee, leverage=lev, asset=asset, tf=tf)
|
||||
return full, m_is, m_oos
|
||||
|
||||
|
||||
def buy_hold(df, cut=None):
|
||||
c = df["close"].values
|
||||
if cut is None:
|
||||
cut = oos_split(df, 0.65)
|
||||
return c[-1] / c[0] - 1, c[-1] / c[cut] - 1 # (full, oos)
|
||||
|
||||
|
||||
def print_benchmarks():
|
||||
print("\n" + "=" * 110)
|
||||
print("# BUY & HOLD BENCHMARK (the bar any long/short trend edge must clear)")
|
||||
print("# NOTE: OOS window is the LAST 35% = ~late-2023 -> 2026, a single (mostly bull) regime.")
|
||||
print("# 2018-2022 (bear+crash+bull+bear) is ENTIRELY in-sample. 'positive OOS' is weak evidence.")
|
||||
print("=" * 110)
|
||||
for tf in TFS:
|
||||
for asset in ASSETS:
|
||||
df = load(asset, tf)
|
||||
cut = oos_split(df, 0.65)
|
||||
bf, bo = buy_hold(df, cut)
|
||||
print(f" {asset} {tf:>3s} OOS starts {df['datetime'].iloc[cut].date()} "
|
||||
f"B&H full={bf*100:>+7.0f}% B&H OOS={bo*100:>+7.0f}%")
|
||||
|
||||
|
||||
def line(label, m):
|
||||
print(f" {label:<30s} tr={m.n_trades:>6d} wr={m.win_rate:>4.1f}% "
|
||||
f"ret={m.net_return*100:>+8.0f}% CAGR={m.cagr*100:>+6.1f}% "
|
||||
f"Sh={m.sharpe:>5.2f} DD={m.max_dd*100:>4.1f}% mkt={m.time_in_market*100:>3.0f}% "
|
||||
f"€/d={m.daily_profit(2000):>+6.2f}")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Experiments
|
||||
# ---------------------------------------------------------------------------
|
||||
def run_grid(name, builder, param_grid, builder_kwargs_fn, tfs=TFS, assets=ASSETS):
|
||||
"""Generic grid runner. Prints OOS-focused table. Returns list of result dicts."""
|
||||
print("\n" + "=" * 110)
|
||||
print(f"# {name}")
|
||||
print("=" * 110)
|
||||
results = []
|
||||
for tf in tfs:
|
||||
for asset in assets:
|
||||
df = load(asset, tf)
|
||||
print(f"\n -- {asset} {tf} (n={len(df)}) --")
|
||||
for params in param_grid:
|
||||
ent = builder(df, **builder_kwargs_fn(params))
|
||||
full, m_is, m_oos = eval_is_oos(df, ent, asset, tf)
|
||||
tag = ",".join(f"{k}={v}" for k, v in params.items())
|
||||
line(f"{tag} [OOS]", m_oos)
|
||||
results.append(dict(name=name, asset=asset, tf=tf, params=params,
|
||||
full=full, is_=m_is, oos=m_oos))
|
||||
return results
|
||||
|
||||
|
||||
def summarize_survivors(all_results):
|
||||
print("\n" + "#" * 110)
|
||||
print("# SURVIVOR SCREEN — positive OOS net return AND positive full-sample, Sharpe(OOS)>0")
|
||||
print("#" * 110)
|
||||
survivors = [r for r in all_results
|
||||
if r["oos"].net_return > 0 and r["full"].net_return > 0
|
||||
and r["oos"].sharpe > 0 and r["oos"].n_trades >= 20]
|
||||
if not survivors:
|
||||
print(" NONE. No config is net-positive OOS with positive full-sample and Sharpe>0.")
|
||||
return []
|
||||
survivors.sort(key=lambda r: r["oos"].sharpe, reverse=True)
|
||||
# precompute B&H OOS per (asset,tf)
|
||||
bh = {}
|
||||
for tf in TFS:
|
||||
for a in ASSETS:
|
||||
bh[(a, tf)] = buy_hold(load(a, tf))[1]
|
||||
print(" (BEATS B&H = OOS return exceeds buy&hold over same OOS window; otherwise it's just beta)")
|
||||
for r in survivors[:40]:
|
||||
tag = ",".join(f"{k}={v}" for k, v in r["params"].items())
|
||||
bho = bh[(r["asset"], r["tf"])]
|
||||
beat = "BEATS B&H" if r["oos"].net_return > bho else "<= B&H (beta)"
|
||||
print(f" {r['name'][:18]:<18s} {r['asset']} {r['tf']:>3s} {tag:<28s} "
|
||||
f"OOS: ret={r['oos'].net_return*100:>+7.0f}% Sh={r['oos'].sharpe:>4.2f} "
|
||||
f"DD={r['oos'].max_dd*100:>4.0f}% €/d={r['oos'].daily_profit(2000):>+5.2f} | "
|
||||
f"B&H={bho*100:>+5.0f}% {beat}")
|
||||
return survivors
|
||||
|
||||
|
||||
def robustness_report(survivors):
|
||||
"""For top survivors, check fee sweep + leverage stress + cross-asset consistency."""
|
||||
if not survivors:
|
||||
return
|
||||
print("\n" + "#" * 110)
|
||||
print("# ROBUSTNESS: fee sweep (0.0005/0.001/0.0015/0.002) + leverage (1x/2x/3x) on top survivors")
|
||||
print("#" * 110)
|
||||
seen = set()
|
||||
for r in survivors[:8]:
|
||||
key = (r["name"], r["asset"], r["tf"], tuple(r["params"].items()))
|
||||
if key in seen:
|
||||
continue
|
||||
seen.add(key)
|
||||
df = load(r["asset"], r["tf"])
|
||||
# rebuild entries
|
||||
builder = BUILDERS[r["name"]]
|
||||
ent = builder(df, **KW_FN[r["name"]](r["params"]))
|
||||
tag = ",".join(f"{k}={v}" for k, v in r["params"].items())
|
||||
print(f"\n {r['name']} {r['asset']} {r['tf']} {tag}")
|
||||
print(" fee sweep (OOS net return):")
|
||||
for fee in (0.0005, 0.001, 0.0015, 0.002):
|
||||
_, _, m_oos = eval_is_oos(df, ent, r["asset"], r["tf"], fee=fee)
|
||||
flag = "" if m_oos.net_return > 0 else " <-- DIES"
|
||||
print(f" fee={fee:.4f}: OOS ret={m_oos.net_return*100:>+8.0f}% Sh={m_oos.sharpe:>4.2f}{flag}")
|
||||
print(" leverage stress (OOS, fee=0.001):")
|
||||
for lev in (1.0, 2.0, 3.0):
|
||||
_, _, m_oos = eval_is_oos(df, ent, r["asset"], r["tf"], lev=lev)
|
||||
print(f" {lev:.0f}x: OOS ret={m_oos.net_return*100:>+8.0f}% "
|
||||
f"Sh={m_oos.sharpe:>4.2f} DD={m_oos.max_dd*100:>4.0f}% €/d={m_oos.daily_profit(2000):>+5.2f}")
|
||||
# yearly OOS
|
||||
_, _, m_oos = eval_is_oos(df, ent, r["asset"], r["tf"])
|
||||
print(" OOS yearly:")
|
||||
for y in sorted(m_oos.yearly):
|
||||
print(f" {y}: {m_oos.yearly[y]*100:>+7.1f}%")
|
||||
|
||||
|
||||
# registry so robustness_report can rebuild entries
|
||||
BUILDERS = {
|
||||
"TSMOM": sig_tsmom,
|
||||
"TSMOM_LONG": sig_tsmom,
|
||||
"EMA_CROSS": sig_ema_cross,
|
||||
"DONCHIAN": sig_donchian,
|
||||
"VOLSCALED_TSMOM": sig_vol_scaled_tsmom,
|
||||
}
|
||||
KW_FN = {
|
||||
"TSMOM": lambda p: dict(lookback=p["N"], hold=p["H"]),
|
||||
"TSMOM_LONG": lambda p: dict(lookback=p["N"], hold=p["H"], long_only=True),
|
||||
"EMA_CROSS": lambda p: dict(fast=p["f"], slow=p["s"], hold=p["H"]),
|
||||
"DONCHIAN": lambda p: dict(lookback=p["N"], hold=p["H"]),
|
||||
"VOLSCALED_TSMOM": lambda p: dict(lookback=p["N"], hold=p["H"], vol_win=p["vw"], z_gate=p["z"]),
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
pd.set_option("display.width", 200)
|
||||
print_benchmarks()
|
||||
all_results = []
|
||||
|
||||
# ---- 1. TSMOM (long/short) ----
|
||||
tsmom_grid = [dict(N=n, H=h) for n in (10, 20, 50, 100, 200) for h in (6, 12, 24, 48)]
|
||||
all_results += run_grid("TSMOM", sig_tsmom, tsmom_grid,
|
||||
KW_FN["TSMOM"])
|
||||
|
||||
# ---- 2. TSMOM long-only (crypto has strong upward drift; honest to test) ----
|
||||
all_results += run_grid("TSMOM_LONG", lambda df, **k: sig_tsmom(df, long_only=True, **k),
|
||||
[dict(N=n, H=h) for n in (20, 50, 100, 200) for h in (12, 24, 48)],
|
||||
KW_FN["TSMOM"])
|
||||
|
||||
# ---- 3. EMA crossover ----
|
||||
ema_grid = [dict(f=f, s=s, H=h)
|
||||
for (f, s) in ((10, 30), (20, 50), (20, 100), (50, 200))
|
||||
for h in (12, 24, 48)]
|
||||
all_results += run_grid("EMA_CROSS", sig_ema_cross, ema_grid, KW_FN["EMA_CROSS"])
|
||||
|
||||
# ---- 4. Donchian breakout ----
|
||||
don_grid = [dict(N=n, H=h) for n in (20, 50, 100, 200) for h in (12, 24, 48)]
|
||||
all_results += run_grid("DONCHIAN", sig_donchian, don_grid, KW_FN["DONCHIAN"])
|
||||
|
||||
# ---- 5. Vol-scaled / regime-gated TSMOM ----
|
||||
vs_grid = [dict(N=n, H=h, vw=vw, z=z)
|
||||
for n in (20, 50, 100) for h in (24, 48)
|
||||
for vw in (50, 100) for z in (0.5, 1.0)]
|
||||
all_results += run_grid("VOLSCALED_TSMOM", sig_vol_scaled_tsmom, vs_grid,
|
||||
KW_FN["VOLSCALED_TSMOM"])
|
||||
|
||||
# ---- survivor screen + robustness ----
|
||||
survivors = summarize_survivors(all_results)
|
||||
robustness_report(survivors)
|
||||
|
||||
# ---- cross-asset robustness note ----
|
||||
print("\n" + "#" * 110)
|
||||
print("# CROSS-ASSET / CROSS-TF CONSISTENCY of survivors (a real edge holds on BOTH BTC & ETH)")
|
||||
print("#" * 110)
|
||||
from collections import defaultdict
|
||||
by_strat = defaultdict(list)
|
||||
for r in survivors:
|
||||
by_strat[(r["name"], r["tf"], tuple(r["params"].items()))].append(r["asset"])
|
||||
both = [(k, v) for k, v in by_strat.items() if set(v) >= {"BTC", "ETH"}]
|
||||
if not both:
|
||||
print(" No single (strategy, tf, params) cell is an OOS survivor on BOTH BTC and ETH.")
|
||||
print(" => any apparent edge is asset/regime-specific, not a robust trend edge.")
|
||||
else:
|
||||
for (name, tf, params), assets in both:
|
||||
print(f" {name} {tf} {dict(params)} survives on: {assets}")
|
||||
|
||||
print("\nDONE. Read the survivor screen + robustness above for the honest verdict.")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,398 @@
|
||||
"""TRACK B — Machine-learning / feature-prediction on BTC & ETH (Deribit-certified).
|
||||
|
||||
Honest, strict walk-forward ML research. The whole point is to NOT repeat the death of
|
||||
the old library (look-ahead). Everything here obeys:
|
||||
|
||||
* Features for bar i use ONLY data <= close[i] (all rolling windows are backward).
|
||||
* Labels (sign of forward return over H bars) use close[i+H]; in walk-forward we only
|
||||
train on samples whose label is FULLY realized in the past relative to the prediction
|
||||
bar (a gap of H is enforced between train-end and the prediction block).
|
||||
* Scaler + model are fit ONLY on past data, retrained periodically, never on the future.
|
||||
* Net of fees (fee_rt sweep 0.0005 .. 0.002, baseline 0.001). Turnover reported.
|
||||
* Grid over W (lookback for training), H (horizon), threshold, asset, tf.
|
||||
* A final held-out segment (last HELD_OUT_FRAC) is NEVER used to choose configs;
|
||||
configs are selected on the DEV portion, then confirmed once on the held-out tail.
|
||||
|
||||
Run: uv run python scripts/research/trackB_ml.py
|
||||
uv run python scripts/research/trackB_ml.py --quick (smaller grid, faster)
|
||||
uv run python scripts/research/trackB_ml.py --gbm (also try GradientBoosting)
|
||||
|
||||
Entry convention (harness): for a signalled bar i we open at close[i] in the predicted
|
||||
direction and hold up to H bars (max_bars=H, no TP/SL) — a pure test of directional sign.
|
||||
No-overlap is enforced by the harness, so trades are naturally spaced >= H bars.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import sys
|
||||
import time
|
||||
import warnings
|
||||
from pathlib import Path
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
|
||||
from src.backtest.harness import backtest_signals, load
|
||||
|
||||
warnings.filterwarnings("ignore")
|
||||
|
||||
HELD_OUT_FRAC = 0.25 # final tail reserved for confirmation only
|
||||
RETRAIN_K = 250 # retrain every K bars (block prediction)
|
||||
MIN_TRAIN = 400 # minimum usable training samples
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Feature engineering — ALL backward-looking (safe at close[i])
|
||||
# ---------------------------------------------------------------------------
|
||||
def _rsi(close: pd.Series, n: int = 14) -> pd.Series:
|
||||
d = close.diff()
|
||||
up = d.clip(lower=0).ewm(alpha=1 / n, adjust=False).mean()
|
||||
dn = (-d.clip(upper=0)).ewm(alpha=1 / n, adjust=False).mean()
|
||||
rs = up / dn.replace(0, np.nan)
|
||||
return (100 - 100 / (1 + rs)).fillna(50.0)
|
||||
|
||||
|
||||
def _atr(df: pd.DataFrame, n: int = 14) -> pd.Series:
|
||||
h, l, c = df["high"], df["low"], df["close"]
|
||||
pc = c.shift(1)
|
||||
tr = pd.concat([(h - l), (h - pc).abs(), (l - pc).abs()], axis=1).max(axis=1)
|
||||
return tr.ewm(alpha=1 / n, adjust=False).mean()
|
||||
|
||||
|
||||
def build_features(df: pd.DataFrame) -> tuple[np.ndarray, list[str], np.ndarray]:
|
||||
"""Return (X, names, warmup_valid_mask). Every column known at close[i]."""
|
||||
c = df["close"].astype(float)
|
||||
h = df["high"].astype(float)
|
||||
l = df["low"].astype(float)
|
||||
o = df["open"].astype(float)
|
||||
v = df["volume"].astype(float)
|
||||
logc = np.log(c)
|
||||
|
||||
feats: dict[str, pd.Series] = {}
|
||||
|
||||
# multi-lag simple returns (ret[i] uses close[i],close[i-k] -> known at i)
|
||||
for k in (1, 2, 3, 6, 12, 24):
|
||||
feats[f"ret{k}"] = c.pct_change(k)
|
||||
|
||||
# candle geometry (current bar fully known at its close)
|
||||
rng = (h - l).replace(0, np.nan)
|
||||
feats["body"] = (c - o) / rng
|
||||
feats["upsh"] = (h - np.maximum(c, o)) / rng
|
||||
feats["dnsh"] = (np.minimum(c, o) - l) / rng
|
||||
feats["range_n"] = (h - l) / c
|
||||
# one-lag candle geometry
|
||||
feats["body1"] = ((c - o) / rng).shift(1)
|
||||
|
||||
# momentum/acceleration
|
||||
feats["mom48"] = c.pct_change(48)
|
||||
feats["accel"] = c.pct_change(6) - c.pct_change(12)
|
||||
|
||||
# RSI
|
||||
feats["rsi14"] = _rsi(c, 14) / 100.0
|
||||
|
||||
# ATR-normalized extension from a trend baseline
|
||||
ema = c.ewm(span=24, adjust=False).mean()
|
||||
atr = _atr(df, 14)
|
||||
feats["ext_atr"] = (c - ema) / atr.replace(0, np.nan)
|
||||
|
||||
# realized vol (std of 1-bar returns)
|
||||
r1 = c.pct_change()
|
||||
feats["rvol24"] = r1.rolling(24).std()
|
||||
feats["rvol72"] = r1.rolling(72).std()
|
||||
feats["vol_ratio"] = feats["rvol24"] / feats["rvol72"].replace(0, np.nan)
|
||||
|
||||
# position of close within recent window (0=low,1=high)
|
||||
for w in (24, 72):
|
||||
lo = l.rolling(w).min()
|
||||
hi = h.rolling(w).max()
|
||||
feats[f"pos{w}"] = (c - lo) / (hi - lo).replace(0, np.nan)
|
||||
|
||||
# volume z-score
|
||||
vlog = np.log1p(v)
|
||||
feats["volz"] = (vlog - vlog.rolling(72).mean()) / vlog.rolling(72).std().replace(0, np.nan)
|
||||
|
||||
names = list(feats.keys())
|
||||
X = np.column_stack([feats[k].to_numpy(dtype=float) for k in names])
|
||||
valid = np.isfinite(X).all(axis=1)
|
||||
return X, names, valid
|
||||
|
||||
|
||||
def forward_labels(df: pd.DataFrame, H: int):
|
||||
"""label[i] = 1 if close[i+H] > close[i] else 0 ; fwd[i] = forward return."""
|
||||
c = df["close"].to_numpy(float)
|
||||
n = len(c)
|
||||
fwd = np.full(n, np.nan)
|
||||
fwd[: n - H] = c[H:] / c[: n - H] - 1.0
|
||||
y = (fwd > 0).astype(float)
|
||||
lab_valid = np.isfinite(fwd)
|
||||
return y, fwd, lab_valid
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Strict walk-forward probability
|
||||
# ---------------------------------------------------------------------------
|
||||
def walk_forward_proba(X, y, feat_valid, lab_valid, warmup, W, H, K, model_factory):
|
||||
"""Return proba_up[i] for all i (NaN where not predicted). No leakage:
|
||||
when predicting block starting at b, training labels must be realized: i + H <= b-1,
|
||||
i.e. train indices < b - H. Training window is the last W such indices."""
|
||||
n = len(y)
|
||||
proba = np.full(n, np.nan)
|
||||
start = warmup + W + H
|
||||
b = start
|
||||
while b < n:
|
||||
end_block = min(b + K, n)
|
||||
train_hi = b - H # exclusive; ensures label realized by b-1
|
||||
train_lo = max(warmup, train_hi - W)
|
||||
idx = np.arange(train_lo, train_hi)
|
||||
idx = idx[feat_valid[idx] & lab_valid[idx]]
|
||||
if len(idx) >= MIN_TRAIN:
|
||||
ytr = y[idx]
|
||||
if np.unique(ytr).size == 2:
|
||||
Xtr = X[idx]
|
||||
sc = StandardScaler().fit(Xtr)
|
||||
model = model_factory()
|
||||
model.fit(sc.transform(Xtr), ytr)
|
||||
# predict the block (features known at each bar's own close)
|
||||
blk = np.arange(b, end_block)
|
||||
fv = feat_valid[blk]
|
||||
if fv.any():
|
||||
pb = model.predict_proba(sc.transform(X[blk[fv]]))[:, 1]
|
||||
proba[blk[fv]] = pb
|
||||
b = end_block
|
||||
return proba
|
||||
|
||||
|
||||
def proba_to_entries(proba, threshold, H, n):
|
||||
"""Long if proba>0.5+thr, short if proba<0.5-thr, else flat. Hold H bars."""
|
||||
entries = [None] * n
|
||||
hi = 0.5 + threshold
|
||||
lo = 0.5 - threshold
|
||||
for i in range(n):
|
||||
p = proba[i]
|
||||
if not np.isfinite(p):
|
||||
continue
|
||||
if p > hi:
|
||||
entries[i] = {"dir": 1, "tp": None, "sl": None, "max_bars": H}
|
||||
elif p < lo:
|
||||
entries[i] = {"dir": -1, "tp": None, "sl": None, "max_bars": H}
|
||||
return entries
|
||||
|
||||
|
||||
def mask_entries(entries, lo, hi):
|
||||
"""Keep only entries with index in [lo, hi); others -> None (for IS/OOS split)."""
|
||||
out = [None] * len(entries)
|
||||
for i in range(lo, min(hi, len(entries))):
|
||||
out[i] = entries[i]
|
||||
return out
|
||||
|
||||
|
||||
def trade_stats(df, entries, H):
|
||||
"""Replicate harness no-overlap to get per-trade gross returns -> avg win/loss + long frac."""
|
||||
c = df["close"].to_numpy(float)
|
||||
n = len(c)
|
||||
grosses = []
|
||||
dirs = []
|
||||
busy = -1
|
||||
for i in range(n):
|
||||
e = entries[i]
|
||||
if e is None or i <= busy:
|
||||
continue
|
||||
j = min(i + H, n - 1)
|
||||
g = (c[j] - c[i]) / c[i] * e["dir"]
|
||||
grosses.append(g)
|
||||
dirs.append(e["dir"])
|
||||
busy = j
|
||||
g = np.array(grosses)
|
||||
if len(g) == 0:
|
||||
return 0, 0.0, 0.0, 0.0, 0.0
|
||||
wins = g[g > 0]
|
||||
losses = g[g <= 0]
|
||||
avg_w = wins.mean() if len(wins) else 0.0
|
||||
avg_l = losses.mean() if len(losses) else 0.0
|
||||
long_frac = float(np.mean(np.array(dirs) > 0))
|
||||
return len(g), avg_w, avg_l, g.mean(), long_frac
|
||||
|
||||
|
||||
def buy_hold(df, lo, hi):
|
||||
"""Buy & hold net return over [lo,hi) bars (beta benchmark)."""
|
||||
c = df["close"].to_numpy(float)
|
||||
hi = min(hi, len(c))
|
||||
if hi - lo < 2:
|
||||
return 0.0
|
||||
return c[hi - 1] / c[lo] - 1.0
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Driver
|
||||
# ---------------------------------------------------------------------------
|
||||
def run():
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument("--quick", action="store_true", help="smaller grid (faster)")
|
||||
ap.add_argument("--gbm", action="store_true", help="also try GradientBoosting on best LR cells")
|
||||
ap.add_argument("--tf", default="1h")
|
||||
args = ap.parse_args()
|
||||
|
||||
assets = ["BTC", "ETH"]
|
||||
tf = args.tf
|
||||
if args.quick:
|
||||
Ws = [8000]
|
||||
Hs = [12, 24]
|
||||
thresholds = [0.0, 0.05, 0.10]
|
||||
else:
|
||||
Ws = [4000, 8000, 16000]
|
||||
Hs = [6, 12, 24, 48]
|
||||
thresholds = [0.0, 0.03, 0.06, 0.10]
|
||||
|
||||
def lr_factory():
|
||||
return LogisticRegression(C=1.0, max_iter=300, class_weight="balanced")
|
||||
|
||||
print("=" * 100)
|
||||
print(f"TRACK B — walk-forward ML tf={tf} retrain_K={RETRAIN_K} held_out_tail={HELD_OUT_FRAC:.0%}")
|
||||
print(f" Ws={Ws} Hs={Hs} thresholds={thresholds} model=LogisticRegression(balanced)")
|
||||
print("=" * 100)
|
||||
|
||||
# cache features per asset
|
||||
cache = {}
|
||||
for a in assets:
|
||||
df = load(a, tf)
|
||||
X, names, fvalid = build_features(df)
|
||||
warmup = int(np.argmax(fvalid)) if fvalid.any() else 0
|
||||
cache[a] = (df, X, names, fvalid, warmup)
|
||||
print(f"features ({len(names)}): {names}\n")
|
||||
|
||||
# ---- DEV grid search (configs chosen ONLY on dev portion) ----------------
|
||||
results = [] # dict rows
|
||||
t0 = time.time()
|
||||
for a in assets:
|
||||
df, X, names, fvalid, warmup = cache[a]
|
||||
n = len(df)
|
||||
dev_hi = int(n * (1 - HELD_OUT_FRAC)) # dev = [0, dev_hi), held = [dev_hi, n)
|
||||
for W in Ws:
|
||||
for H in Hs:
|
||||
y, _fwd, lvalid = forward_labels(df, H)
|
||||
proba = walk_forward_proba(X, y, fvalid, lvalid, warmup, W, H,
|
||||
RETRAIN_K, lr_factory)
|
||||
for thr in thresholds:
|
||||
ent_full = proba_to_entries(proba, thr, H, n)
|
||||
ent_dev = mask_entries(ent_full, warmup, dev_hi)
|
||||
m = backtest_signals(df, ent_dev, fee_rt=0.001, asset=a, tf=tf)
|
||||
nt, aw, al, gmean, lf = trade_stats(df, ent_dev, H)
|
||||
results.append(dict(asset=a, W=W, H=H, thr=thr, seg="DEV",
|
||||
m=m, nt=nt, aw=aw, al=al, gmean=gmean,
|
||||
proba=proba))
|
||||
print(f" [{a}] dev grid done ({time.time()-t0:.0f}s)")
|
||||
|
||||
# print dev table
|
||||
print("\n--- DEV walk-forward (config selection set) ---")
|
||||
hdr = f"{'asset':5} {'W':>6} {'H':>3} {'thr':>5} {'trd':>5} {'wr%':>5} {'net%':>8} {'CAGR%':>7} {'Shrp':>6} {'DD%':>5} {'mkt%':>5} {'avgW%':>6} {'avgL%':>6} {'€/d':>6}"
|
||||
print(hdr)
|
||||
for r in sorted(results, key=lambda r: -r["m"].sharpe):
|
||||
m = r["m"]
|
||||
print(f"{r['asset']:5} {r['W']:>6} {r['H']:>3} {r['thr']:>5.2f} {m.n_trades:>5} "
|
||||
f"{m.win_rate:>5.1f} {m.net_return*100:>+8.1f} {m.cagr*100:>+7.1f} {m.sharpe:>6.2f} "
|
||||
f"{m.max_dd*100:>5.1f} {m.time_in_market*100:>5.0f} {r['aw']*100:>+6.2f} {r['al']*100:>+6.2f} "
|
||||
f"{m.daily_profit(2000):>+6.2f}")
|
||||
|
||||
# ---- selection: positive net AND sharpe>0 on dev, then robustness ----------
|
||||
pos = [r for r in results if r["m"].net_return > 0 and r["m"].sharpe > 0 and r["m"].n_trades >= 30]
|
||||
pos.sort(key=lambda r: -r["m"].sharpe)
|
||||
print(f"\n{len(pos)}/{len(results)} dev cells net-positive with Sharpe>0 & >=30 trades.")
|
||||
|
||||
# robustness: a config family (asset,W,H) is robust if positive across thresholds
|
||||
fam = {}
|
||||
for r in results:
|
||||
fam.setdefault((r["asset"], r["W"], r["H"]), []).append(r)
|
||||
robust_fams = []
|
||||
for key, rs in fam.items():
|
||||
npos = sum(1 for r in rs if r["m"].net_return > 0 and r["m"].sharpe > 0)
|
||||
if npos >= max(2, int(0.6 * len(rs))):
|
||||
robust_fams.append((key, npos, len(rs)))
|
||||
robust_fams.sort(key=lambda x: -x[1])
|
||||
print("\nThreshold-robust (asset,W,H) families [>=60% thresholds net+ & Sharpe>0]:")
|
||||
if not robust_fams:
|
||||
print(" NONE.")
|
||||
for key, npos, tot in robust_fams:
|
||||
print(f" {key}: {npos}/{tot} thresholds positive")
|
||||
|
||||
# ---- HELD-OUT confirmation on best robust cells ---------------------------
|
||||
print("\n" + "=" * 100)
|
||||
print("HELD-OUT TAIL CONFIRMATION (never used for selection)")
|
||||
print("=" * 100)
|
||||
# choose up to 6 best dev cells that belong to a robust family
|
||||
robust_keys = {k for k, _, _ in robust_fams}
|
||||
cand = [r for r in pos if (r["asset"], r["W"], r["H"]) in robust_keys][:6]
|
||||
if not cand:
|
||||
cand = pos[:6]
|
||||
if not cand:
|
||||
print("No positive dev cells to confirm. ML did not beat fees on dev.")
|
||||
print(hdr)
|
||||
held_rows = []
|
||||
for r in cand:
|
||||
a, W, H, thr = r["asset"], r["W"], r["H"], r["thr"]
|
||||
df = cache[a][0]
|
||||
n = len(df)
|
||||
dev_hi = int(n * (1 - HELD_OUT_FRAC))
|
||||
ent_full = proba_to_entries(r["proba"], thr, H, n)
|
||||
ent_held = mask_entries(ent_full, dev_hi, n)
|
||||
m = backtest_signals(df, ent_held, fee_rt=0.001, asset=a, tf=tf)
|
||||
nt, aw, al, gmean, lf = trade_stats(df, ent_held, H)
|
||||
bh = buy_hold(df, dev_hi, n)
|
||||
held_rows.append((r, m, aw, al, lf, bh))
|
||||
print(f"{a:5} {W:>6} {H:>3} {thr:>5.2f} {m.n_trades:>5} {m.win_rate:>5.1f} "
|
||||
f"{m.net_return*100:>+8.1f} {m.cagr*100:>+7.1f} {m.sharpe:>6.2f} {m.max_dd*100:>5.1f} "
|
||||
f"{m.time_in_market*100:>5.0f} {aw*100:>+6.2f} {al*100:>+6.2f} {m.daily_profit(2000):>+6.2f} "
|
||||
f"long={lf*100:>3.0f}% B&H={bh*100:>+7.1f}%")
|
||||
|
||||
# ---- FEE SWEEP on the held-out winners ------------------------------------
|
||||
print("\n--- FEE SWEEP (held-out tail) on confirmed cells ---")
|
||||
fees = [0.0005, 0.001, 0.0015, 0.002]
|
||||
print(" (B&H = buy&hold over held-out tail; if net% << B&H the 'edge' is just beta)")
|
||||
for r, _, _, _, _, _ in held_rows[:4]:
|
||||
a, W, H, thr = r["asset"], r["W"], r["H"], r["thr"]
|
||||
df = cache[a][0]
|
||||
n = len(df)
|
||||
dev_hi = int(n * (1 - HELD_OUT_FRAC))
|
||||
ent_held = mask_entries(proba_to_entries(r["proba"], thr, H, n), dev_hi, n)
|
||||
line = f" {a} W{W} H{H} thr{thr:.2f}: "
|
||||
for f in fees:
|
||||
m = backtest_signals(df, ent_held, fee_rt=f, asset=a, tf=tf)
|
||||
line += f"[{f*100:.2f}%]net={m.net_return*100:>+6.1f}% Shrp={m.sharpe:>+4.2f} "
|
||||
print(line)
|
||||
|
||||
# ---- per-year on the single best held-out cell ----------------------------
|
||||
if held_rows:
|
||||
held_rows.sort(key=lambda x: -x[1].sharpe)
|
||||
r, m, aw, al, lf, bh = held_rows[0]
|
||||
a, W, H, thr = r["asset"], r["W"], r["H"], r["thr"]
|
||||
print(f"\n--- Per-year (best held-out): {a} W{W} H{H} thr{thr:.2f} ---")
|
||||
df = cache[a][0]
|
||||
n = len(df)
|
||||
dev_hi = int(n * (1 - HELD_OUT_FRAC))
|
||||
# full walk-forward per-year (dev+held) to see regime stability
|
||||
mfull = backtest_signals(df, mask_entries(proba_to_entries(r["proba"], thr, H, n),
|
||||
cache[a][4], n), fee_rt=0.001, asset=a, tf=tf)
|
||||
mfull.print_summary(f"{a} W{W}H{H}thr{thr:.2f} FULL-WF")
|
||||
mfull.print_yearly()
|
||||
|
||||
print(f"\nTotal runtime {time.time()-t0:.0f}s")
|
||||
print("\n" + "=" * 100)
|
||||
print("VERDICT (see docs/diary/2026-06-19-trackB-ml.md for the full write-up)")
|
||||
print("=" * 100)
|
||||
print(
|
||||
" * A weak but REAL low-turnover directional signal exists on BTC (thinner on ETH):\n"
|
||||
" large train window (W~16000) + long horizon (H~24) + high prob threshold (~0.10).\n"
|
||||
" * It beats fees at 0.10% RT AND beats buy&hold on the held-out tail with a balanced\n"
|
||||
" long/short mix (so it is NOT just bull-market beta). Payoff: ~53% WR, avgWin>avgLoss.\n"
|
||||
" * BUT: high-turnover cells (low thr / short H / 15m) ALL die on fees -> the edge is small.\n"
|
||||
" Returns concentrate in a few years (2021,2025) with a -38% year (2023); DD 23-56%.\n"
|
||||
" * EUR/day on 2000 ~= +0.3..+0.6 baseline. Target is 50/day -> ~100x short. NOT deployable\n"
|
||||
" standalone; at best a small component, and only the lowest-turnover configs are honest."
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
run()
|
||||
@@ -0,0 +1,380 @@
|
||||
"""TRACK C — Mean-reversion / range re-examination on CLEAN BTC/ETH (Deribit mainnet).
|
||||
|
||||
HONEST harness only. The OLD 'fade' library (Bollinger fade, Donchian fade, return
|
||||
reversal) was an ARTIFACT of look-ahead + ghost wicks on a contaminated feed; on the
|
||||
rebuilt+certified data those are negative every year. This script asks, skeptically:
|
||||
|
||||
Does ANY short-horizon mean-reversion / range edge survive on clean BTC/ETH with a
|
||||
genuinely EXECUTABLE entry (direction + price decided with data <= close[i],
|
||||
fill at close[i]), net of realistic Deribit fees, out-of-sample and grid-robust?
|
||||
|
||||
Methodology enforced here:
|
||||
* Entry decided with data through close[i]; fill at close[i] (harness guarantees it).
|
||||
No entering "at the band edge" / candle extreme only known intrabar.
|
||||
* NET fees fee_rt=0.001 baseline + sweep {0.0005, 0.0015, 0.002}.
|
||||
* OOS 65/35 split + parameter grid across BOTH BTC & ETH.
|
||||
* Liquidity/plausibility cross-check: time-in-market, avg bars, and whether the edge
|
||||
concentrates in flat (O=H=L=C heavy) periods.
|
||||
|
||||
Run:
|
||||
uv run python scripts/research/trackC_meanrev.py # full (slow, all TFs)
|
||||
uv run python scripts/research/trackC_meanrev.py --quick # 1h + 15m only
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import sys
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
|
||||
|
||||
from src.backtest.harness import load, backtest_signals, oos_split, Metrics
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# Indicator helpers — ALL causal: value at index i uses ONLY data through i.
|
||||
# ===========================================================================
|
||||
def zscore(close: np.ndarray, lookback: int) -> np.ndarray:
|
||||
s = pd.Series(close)
|
||||
ma = s.rolling(lookback).mean()
|
||||
sd = s.rolling(lookback).std(ddof=0)
|
||||
z = (s - ma) / sd
|
||||
return z.values, ma.values, sd.values
|
||||
|
||||
|
||||
def rsi(close: np.ndarray, period: int) -> np.ndarray:
|
||||
s = pd.Series(close)
|
||||
d = s.diff()
|
||||
up = d.clip(lower=0.0)
|
||||
dn = (-d).clip(lower=0.0)
|
||||
# Wilder smoothing via ewm alpha=1/period (causal)
|
||||
ru = up.ewm(alpha=1.0 / period, adjust=False).mean()
|
||||
rd = dn.ewm(alpha=1.0 / period, adjust=False).mean()
|
||||
rs = ru / rd.replace(0, np.nan)
|
||||
out = 100 - 100 / (1 + rs)
|
||||
return out.values
|
||||
|
||||
|
||||
def atr(df: pd.DataFrame, period: int) -> np.ndarray:
|
||||
h, l, c = df["high"].values, df["low"].values, df["close"].values
|
||||
pc = np.roll(c, 1)
|
||||
pc[0] = c[0]
|
||||
tr = np.maximum(h - l, np.maximum(np.abs(h - pc), np.abs(l - pc)))
|
||||
return pd.Series(tr).ewm(alpha=1.0 / period, adjust=False).mean().values
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# Signal generators — each returns a list[dict|None] length len(df).
|
||||
# Direction/levels decided strictly with data through close[i].
|
||||
# ===========================================================================
|
||||
def sig_zfade(df, lookback=20, z=2.0, tp_mode="mean", tp_atr=1.0, sl_atr=2.0,
|
||||
max_bars=24, atr_p=14):
|
||||
"""Bollinger / z-score fade. z<-thr -> long (reversion up); z>+thr -> short.
|
||||
TP at the moving mean (tp_mode='mean') or at tp_atr*ATR toward the mean.
|
||||
SL at sl_atr*ATR beyond entry. Entry at close[i]."""
|
||||
c = df["close"].values
|
||||
z_arr, ma, _ = zscore(c, lookback)
|
||||
a = atr(df, atr_p)
|
||||
n = len(c)
|
||||
out = [None] * n
|
||||
for i in range(lookback, n):
|
||||
zi = z_arr[i]
|
||||
if not np.isfinite(zi) or not np.isfinite(a[i]):
|
||||
continue
|
||||
px = c[i]
|
||||
if zi <= -z:
|
||||
direction = 1
|
||||
tp = ma[i] if tp_mode == "mean" else px + tp_atr * a[i]
|
||||
sl = px - sl_atr * a[i] if sl_atr else None
|
||||
elif zi >= z:
|
||||
direction = -1
|
||||
tp = ma[i] if tp_mode == "mean" else px - tp_atr * a[i]
|
||||
sl = px + sl_atr * a[i] if sl_atr else None
|
||||
else:
|
||||
continue
|
||||
# guardrail: never set TP on wrong side of entry
|
||||
if direction == 1 and tp <= px:
|
||||
tp = px + tp_atr * a[i]
|
||||
if direction == -1 and tp >= px:
|
||||
tp = px - tp_atr * a[i]
|
||||
out[i] = {"dir": direction, "tp": tp, "sl": sl, "max_bars": max_bars}
|
||||
return out
|
||||
|
||||
|
||||
def sig_rsi2(df, period=2, lo=10, hi=90, tp_atr=1.0, sl_atr=2.0, max_bars=12,
|
||||
atr_p=14, sma_filter=0):
|
||||
"""RSI(2)-style oversold/overbought reversion. RSI<lo -> long, RSI>hi -> short.
|
||||
Optional trend filter: only long above SMA(sma_filter), only short below."""
|
||||
c = df["close"].values
|
||||
r = rsi(c, period)
|
||||
a = atr(df, atr_p)
|
||||
sma = pd.Series(c).rolling(sma_filter).mean().values if sma_filter else None
|
||||
n = len(c)
|
||||
out = [None] * n
|
||||
for i in range(max(period, atr_p, sma_filter), n):
|
||||
ri = r[i]
|
||||
if not np.isfinite(ri) or not np.isfinite(a[i]):
|
||||
continue
|
||||
px = c[i]
|
||||
if ri <= lo:
|
||||
if sma is not None and not (px > sma[i]):
|
||||
continue
|
||||
out[i] = {"dir": 1, "tp": px + tp_atr * a[i],
|
||||
"sl": px - sl_atr * a[i] if sl_atr else None, "max_bars": max_bars}
|
||||
elif ri >= hi:
|
||||
if sma is not None and not (px < sma[i]):
|
||||
continue
|
||||
out[i] = {"dir": -1, "tp": px - tp_atr * a[i],
|
||||
"sl": px + sl_atr * a[i] if sl_atr else None, "max_bars": max_bars}
|
||||
return out
|
||||
|
||||
|
||||
def sig_retrev(df, ret_lb=1, thr_sigma=2.0, vol_lb=50, tp_atr=1.0, sl_atr=2.0,
|
||||
max_bars=6, atr_p=14):
|
||||
"""Return reversal: fade an extreme cumulative return over the last ret_lb bars.
|
||||
Extreme = |ret| > thr_sigma * rolling std of that return. Entry at close[i]."""
|
||||
c = df["close"].values
|
||||
s = pd.Series(c)
|
||||
ret = np.log(s / s.shift(ret_lb))
|
||||
sd = ret.rolling(vol_lb).std(ddof=0)
|
||||
a = atr(df, atr_p)
|
||||
n = len(c)
|
||||
out = [None] * n
|
||||
rv = ret.values
|
||||
sv = sd.values
|
||||
for i in range(vol_lb + ret_lb, n):
|
||||
if not np.isfinite(rv[i]) or not np.isfinite(sv[i]) or sv[i] == 0 or not np.isfinite(a[i]):
|
||||
continue
|
||||
z = rv[i] / sv[i]
|
||||
px = c[i]
|
||||
if z <= -thr_sigma:
|
||||
out[i] = {"dir": 1, "tp": px + tp_atr * a[i],
|
||||
"sl": px - sl_atr * a[i] if sl_atr else None, "max_bars": max_bars}
|
||||
elif z >= thr_sigma:
|
||||
out[i] = {"dir": -1, "tp": px - tp_atr * a[i],
|
||||
"sl": px + sl_atr * a[i] if sl_atr else None, "max_bars": max_bars}
|
||||
return out
|
||||
|
||||
|
||||
def sig_vwap(df, sess_bars=24, thr=2.0, tp_atr=1.0, sl_atr=2.0, max_bars=12, atr_p=14):
|
||||
"""Rolling-VWAP distance reversion. Distance in std-of-distance units over a
|
||||
rolling session window. Far above VWAP -> short, far below -> long. Entry close[i]."""
|
||||
c = df["close"].values
|
||||
v = df["volume"].values.astype(float)
|
||||
tp = (df["high"].values + df["low"].values + c) / 3.0
|
||||
pv = pd.Series(tp * v)
|
||||
vol = pd.Series(v)
|
||||
vwap = (pv.rolling(sess_bars).sum() / vol.rolling(sess_bars).sum()).values
|
||||
dist = pd.Series(c - vwap)
|
||||
dsd = dist.rolling(sess_bars).std(ddof=0).values
|
||||
a = atr(df, atr_p)
|
||||
n = len(c)
|
||||
out = [None] * n
|
||||
for i in range(sess_bars * 2, n):
|
||||
if not np.isfinite(vwap[i]) or not np.isfinite(dsd[i]) or dsd[i] == 0 or not np.isfinite(a[i]):
|
||||
continue
|
||||
z = (c[i] - vwap[i]) / dsd[i]
|
||||
px = c[i]
|
||||
if z <= -thr:
|
||||
out[i] = {"dir": 1, "tp": px + tp_atr * a[i],
|
||||
"sl": px - sl_atr * a[i] if sl_atr else None, "max_bars": max_bars}
|
||||
elif z >= thr:
|
||||
out[i] = {"dir": -1, "tp": px - tp_atr * a[i],
|
||||
"sl": px + sl_atr * a[i] if sl_atr else None, "max_bars": max_bars}
|
||||
return out
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# Evaluation utilities
|
||||
# ===========================================================================
|
||||
def flat_fraction(df: pd.DataFrame) -> float:
|
||||
o, h, l, c = df["open"], df["high"], df["low"], df["close"]
|
||||
return float(((h == l) & (o == c)).mean())
|
||||
|
||||
|
||||
def run_split(df, sigfn, params, fee_rt=0.001, leverage=1.0):
|
||||
"""Run full / IS / OOS for a single config. Returns (full, is_, oos)."""
|
||||
entries = sigfn(df, **params)
|
||||
cut = oos_split(df, 0.65)
|
||||
full = backtest_signals(df, entries, fee_rt=fee_rt, leverage=leverage)
|
||||
df_is = df.iloc[:cut].reset_index(drop=True)
|
||||
df_oos = df.iloc[cut:].reset_index(drop=True)
|
||||
is_ = backtest_signals(df_is, sigfn(df_is, **params), fee_rt=fee_rt, leverage=leverage)
|
||||
oos = backtest_signals(df_oos, sigfn(df_oos, **params), fee_rt=fee_rt, leverage=leverage)
|
||||
return full, is_, oos
|
||||
|
||||
|
||||
def hdr(title):
|
||||
print("\n" + "=" * 92)
|
||||
print(title)
|
||||
print("=" * 92)
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# Main
|
||||
# ===========================================================================
|
||||
def main():
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument("--quick", action="store_true", help="1h+15m only (skip slow 5m)")
|
||||
args = ap.parse_args()
|
||||
|
||||
t0 = time.time()
|
||||
tfs = ["1h", "15m"] if args.quick else ["1h", "15m", "5m"]
|
||||
assets = ["BTC", "ETH"]
|
||||
|
||||
# preload + liquidity sanity
|
||||
data = {}
|
||||
hdr("DATA / LIQUIDITY SANITY (flat-bar fraction O=H=L=C; should be ~0 on clean BTC/ETH)")
|
||||
for a in assets:
|
||||
for tf in tfs:
|
||||
df = load(a, tf)
|
||||
data[(a, tf)] = df
|
||||
print(f" {a} {tf:>3s}: {len(df):>7d} bars {df['datetime'].iloc[0].date()}→"
|
||||
f"{df['datetime'].iloc[-1].date()} flat={flat_fraction(df)*100:5.2f}%")
|
||||
|
||||
# -------------------------------------------------------------------
|
||||
# PASS 1 — broad screen per family on 1h, both assets (IS/OOS).
|
||||
# -------------------------------------------------------------------
|
||||
hdr("PASS 1 — FAMILY SCREEN on 1h (honest entry, fee_rt=0.001, lev=1). "
|
||||
"Look for OOS>0 on BOTH assets.")
|
||||
families = {
|
||||
"ZFADE z2/mean ": (sig_zfade, dict(lookback=20, z=2.0, tp_mode="mean", sl_atr=2.0, max_bars=24)),
|
||||
"ZFADE z2.5/atr": (sig_zfade, dict(lookback=20, z=2.5, tp_mode="atr", tp_atr=1.5, sl_atr=2.0, max_bars=24)),
|
||||
"ZFADE z3/mean ": (sig_zfade, dict(lookback=40, z=3.0, tp_mode="mean", sl_atr=3.0, max_bars=48)),
|
||||
"RSI2 10/90 ": (sig_rsi2, dict(period=2, lo=10, hi=90, tp_atr=1.0, sl_atr=2.0, max_bars=12)),
|
||||
"RSI2 5/95 ": (sig_rsi2, dict(period=2, lo=5, hi=95, tp_atr=1.5, sl_atr=2.5, max_bars=12)),
|
||||
"RSI2 +trend ": (sig_rsi2, dict(period=2, lo=10, hi=90, tp_atr=1.0, sl_atr=2.0, max_bars=12, sma_filter=200)),
|
||||
"RETREV 2sig/6b ": (sig_retrev, dict(ret_lb=1, thr_sigma=2.0, tp_atr=1.0, sl_atr=2.0, max_bars=6)),
|
||||
"RETREV 3sig/12b": (sig_retrev, dict(ret_lb=3, thr_sigma=3.0, tp_atr=1.5, sl_atr=2.5, max_bars=12)),
|
||||
"VWAP 2/sess24": (sig_vwap, dict(sess_bars=24, thr=2.0, tp_atr=1.0, sl_atr=2.0, max_bars=12)),
|
||||
}
|
||||
for name, (fn, params) in families.items():
|
||||
line = f" {name} | "
|
||||
for a in assets:
|
||||
df = data[(a, "1h")]
|
||||
full, is_, oos = run_split(df, fn, params)
|
||||
line += (f"{a}: IS={is_.net_return*100:>+6.0f}% OOS={oos.net_return*100:>+6.0f}% "
|
||||
f"(tr={oos.n_trades:>4d} wr={oos.win_rate:>4.1f} shrp={oos.sharpe:>+4.1f} "
|
||||
f"mkt={oos.time_in_market*100:>3.0f}% ab={oos.avg_bars:>4.1f}) ")
|
||||
print(line)
|
||||
|
||||
# -------------------------------------------------------------------
|
||||
# PASS 2 — parameter GRID on the two most-promising families (z-fade, rsi2),
|
||||
# require OOS>0 on BOTH assets to count a cell as "surviving".
|
||||
# -------------------------------------------------------------------
|
||||
hdr("PASS 2 — GRID ROBUSTNESS (1h). A cell 'survives' only if OOS net>0 on BOTH BTC AND ETH.")
|
||||
|
||||
def grid(fn, base, sweep, tf="1h"):
|
||||
keys = list(sweep.keys())
|
||||
survivors = []
|
||||
total = 0
|
||||
rows = []
|
||||
from itertools import product
|
||||
for combo in product(*[sweep[k] for k in keys]):
|
||||
params = dict(base)
|
||||
params.update(dict(zip(keys, combo)))
|
||||
total += 1
|
||||
res = {}
|
||||
for a in assets:
|
||||
_, is_, oos = run_split(data[(a, tf)], fn, params)
|
||||
res[a] = (is_, oos)
|
||||
ok = all(res[a][1].net_return > 0 for a in assets)
|
||||
both_oos = np.mean([res[a][1].net_return for a in assets]) * 100
|
||||
rows.append((params, res, ok))
|
||||
if ok:
|
||||
survivors.append((params, res))
|
||||
print(f" {fn.__name__}: {len(survivors)}/{total} cells with OOS>0 on BOTH assets")
|
||||
# show best few by mean OOS
|
||||
rows.sort(key=lambda r: np.mean([r[1][a][1].net_return for a in assets]), reverse=True)
|
||||
for params, res, ok in rows[:6]:
|
||||
tag = "OK " if ok else " -"
|
||||
pp = {k: params[k] for k in sweep}
|
||||
s = f" {tag} {pp} | "
|
||||
for a in assets:
|
||||
oos = res[a][1]
|
||||
s += f"{a} OOS={oos.net_return*100:>+6.0f}% (wr={oos.win_rate:>4.1f} shrp={oos.sharpe:>+4.1f}) "
|
||||
print(s)
|
||||
return survivors
|
||||
|
||||
zsurv = grid(sig_zfade,
|
||||
dict(tp_mode="mean", max_bars=24),
|
||||
dict(lookback=[20, 40, 60], z=[2.0, 2.5, 3.0], sl_atr=[2.0, 3.0]))
|
||||
rsurv = grid(sig_rsi2,
|
||||
dict(period=2, tp_atr=1.0),
|
||||
dict(lo=[5, 10, 15], hi=[85, 90, 95], sl_atr=[2.0, 3.0], max_bars=[6, 12]))
|
||||
|
||||
# -------------------------------------------------------------------
|
||||
# PASS 3 — FEE SWEEP on whatever looks least-bad (z-fade z2/mean) to show fee
|
||||
# sensitivity (MR is high-frequency: fees are first-order).
|
||||
# -------------------------------------------------------------------
|
||||
hdr("PASS 3 — FEE SWEEP (z-fade lookback=20 z=2 mean, 1h). fee=0 is GROSS: is there\n"
|
||||
" ANY edge before fees, or is the fade direction itself wrong on clean data?")
|
||||
fees = [0.0, 0.0005, 0.001, 0.0015, 0.002]
|
||||
base = dict(lookback=20, z=2.0, tp_mode="mean", sl_atr=2.0, max_bars=24)
|
||||
for a in assets:
|
||||
df = data[(a, "1h")]
|
||||
line = f" {a}: "
|
||||
for f in fees:
|
||||
full, is_, oos = run_split(df, sig_zfade, base, fee_rt=f)
|
||||
line += f"fee={f*1000:.1f}bp→ full={full.net_return*100:>+6.0f}% OOS={oos.net_return*100:>+6.0f}% "
|
||||
print(line)
|
||||
|
||||
# -------------------------------------------------------------------
|
||||
# PASS 4 — faster TFs (15m, 5m) on the canonical z-fade, to test the "more MR
|
||||
# opportunities" hypothesis vs the "fee death" reality.
|
||||
# -------------------------------------------------------------------
|
||||
hdr("PASS 4 — z-fade across timeframes (lookback=20 z=2 mean). Faster TF = more fees.")
|
||||
for tf in tfs:
|
||||
for a in assets:
|
||||
df = data[(a, tf)]
|
||||
full, is_, oos = run_split(df, sig_zfade, base)
|
||||
print(f" {a} {tf:>3s}: full={full.net_return*100:>+7.0f}% IS={is_.net_return*100:>+7.0f}% "
|
||||
f"OOS={oos.net_return*100:>+7.0f}% tr={full.n_trades:>5d} wr={full.win_rate:>4.1f}% "
|
||||
f"shrp={full.sharpe:>+4.1f} mkt={full.time_in_market*100:>3.0f}% €/d={full.daily_profit(2000):>+5.2f}")
|
||||
|
||||
# -------------------------------------------------------------------
|
||||
# PASS 5 — SESSION / overnight effect (UTC hour-of-day) on 1h returns.
|
||||
# Pure descriptive: is there a systematically mean-reverting hour bucket?
|
||||
# -------------------------------------------------------------------
|
||||
hdr("PASS 5 — UTC hour-of-day next-bar return autocorrelation (descriptive, no trade).")
|
||||
for a in assets:
|
||||
df = data[(a, "1h")]
|
||||
c = df["close"].values
|
||||
ret = pd.Series(np.log(c[1:] / c[:-1])) # ret[k] = log(c[k+1]/c[k])
|
||||
prev = ret.shift(1)
|
||||
hours = df["datetime"].dt.hour.values[1:1 + len(ret)]
|
||||
tmp = pd.DataFrame({"h": hours[:len(ret)], "r": ret.values, "p": prev.values}).dropna()
|
||||
# autocorr of consecutive bar returns per hour bucket (negative = mean-reverting)
|
||||
ac = tmp.groupby("h").apply(lambda g: g["r"].corr(g["p"]) if len(g) > 30 else np.nan)
|
||||
worst = ac.nsmallest(3)
|
||||
best = ac.nlargest(3)
|
||||
print(f" {a}: most mean-reverting UTC hours (neg autocorr): "
|
||||
+ ", ".join(f"{int(h)}h={v:+.3f}" for h, v in worst.items())
|
||||
+ " | most trending: "
|
||||
+ ", ".join(f"{int(h)}h={v:+.3f}" for h, v in best.items()))
|
||||
|
||||
# -------------------------------------------------------------------
|
||||
# VERDICT
|
||||
# -------------------------------------------------------------------
|
||||
hdr("VERDICT")
|
||||
n_surv = len(zsurv) + len(rsurv)
|
||||
if n_surv == 0:
|
||||
print(" No grid cell produced OOS net>0 on BOTH BTC and ETH at baseline fees.")
|
||||
print(" => Consistent with the reset thesis: the old MR 'edge' was a feed artifact.")
|
||||
print(" On clean Deribit data with honest executable entry, short-horizon MR is NOT")
|
||||
print(" a robust net-positive edge. (See per-pass tables above for the evidence.)")
|
||||
else:
|
||||
print(f" {n_surv} grid cell(s) survived OOS>0 on both assets. Inspect above; then stress")
|
||||
print(" with fee sweep / faster TFs before believing. Surviving configs:")
|
||||
for params, res in (zsurv + rsurv):
|
||||
ms = np.mean([res[a][1].net_return for a in assets]) * 100
|
||||
print(f" {params} meanOOS={ms:+.0f}%")
|
||||
print(f"\n (elapsed {time.time()-t0:.0f}s)")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,460 @@
|
||||
"""TRACK D — ROBUST WALK-FORWARD TREND PORTFOLIO (BTC+ETH), vol-targeted + leverage.
|
||||
|
||||
Thesis under test: trend-following's real value in crypto is DRAWDOWN REDUCTION vs
|
||||
buy & hold (it sidesteps crashes). That lower DD lets us apply LEVERAGE and DIVERSIFY
|
||||
across BTC+ETH to build a deployable, risk-adjusted EARNING system, even if each single
|
||||
signal has only a modest Sharpe. Question: does a properly-built, anti-overfit trend
|
||||
portfolio actually EARN robustly across regimes 2018-2026?
|
||||
|
||||
METHOD (strict, honest):
|
||||
* NO LOOK-AHEAD. We build equity directly from a TARGET-POSITION series.
|
||||
- target[i] is decided using ONLY data <= close[i].
|
||||
- target[i] is HELD during the next bar (close[i] -> close[i+1]).
|
||||
- bar return r[t] = close[t]/close[t-1] - 1 (uses close[t], close[t-1]; both <= t).
|
||||
- pnl on bar t = target[t-1] * r[t] (shift positions by 1 -> no leakage).
|
||||
- fees: fee_per_side * |target[t-1] - target[t-2]| (turnover cost, charged on rebalances).
|
||||
This is the harness's documented "build your own equity from a position series" path.
|
||||
* VOL-TARGETING: position = directional_signal * (target_vol / realized_vol), capped at
|
||||
leverage. realized_vol uses past returns only (rolling std up to close[i]). This is the
|
||||
main lever — it lets a modest signal run at a controlled risk level.
|
||||
* WALK-FORWARD / MULTI-REGIME: per-year returns for ALL years 2018-2026. Plus an explicit
|
||||
EARLY (2018-2021) tune / LATE (2022-2026) confirm split. ONE robust param set, both assets.
|
||||
* PORTFOLIO: equal-weight BTC+ETH sleeves, rebalanced each bar. Report combined Sharpe/DD/CAGR.
|
||||
* GRID ROBUSTNESS: chosen config must be positive across a neighborhood AND across regimes.
|
||||
* FEE & LEVERAGE SWEEP: fee/side 0.0005..0.002 (0.10..0.40% RT); leverage cap 1x..3x.
|
||||
|
||||
Run: uv run python scripts/research/trackD_trendport.py
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
|
||||
|
||||
from src.backtest.harness import load
|
||||
|
||||
ASSETS = ["BTC", "ETH"]
|
||||
TF = "1h"
|
||||
BARS_PER_YEAR = 24 * 365.25 # 1h bars
|
||||
FEE_SIDE = 0.0005 # 0.05% per side = 0.10% round trip (Deribit taker)
|
||||
|
||||
# horizons in 1h bars ~ 1 / 3 / 6 "months" (30d months)
|
||||
H1, H3, H6 = 30 * 24, 90 * 24, 180 * 24
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Core building blocks (all <= close[i])
|
||||
# ---------------------------------------------------------------------------
|
||||
def simple_returns(c: np.ndarray) -> np.ndarray:
|
||||
r = np.zeros(len(c))
|
||||
r[1:] = c[1:] / c[:-1] - 1.0
|
||||
return r
|
||||
|
||||
|
||||
def realized_vol(r: np.ndarray, win: int) -> np.ndarray:
|
||||
"""Annualized realized vol from bar returns up to and including i (no leakage)."""
|
||||
vol = pd.Series(r).rolling(win, min_periods=win // 2).std().values
|
||||
return vol * np.sqrt(BARS_PER_YEAR)
|
||||
|
||||
|
||||
def sig_tsmom_blend(c: np.ndarray, horizons=(H1, H3, H6)) -> np.ndarray:
|
||||
"""Multi-horizon TSMOM: average of sign(close[i]/close[i-h]-1) over horizons -> [-1,1]."""
|
||||
n = len(c)
|
||||
acc = np.zeros(n)
|
||||
cnt = np.zeros(n)
|
||||
for h in horizons:
|
||||
s = np.full(n, np.nan)
|
||||
s[h:] = np.sign(c[h:] / c[:-h] - 1.0)
|
||||
valid = np.isfinite(s)
|
||||
acc[valid] += s[valid]
|
||||
cnt[valid] += 1
|
||||
out = np.zeros(n)
|
||||
nz = cnt > 0
|
||||
out[nz] = acc[nz] / cnt[nz]
|
||||
return out
|
||||
|
||||
|
||||
def sig_ma_slope(c: np.ndarray, span: int, slope_win: int = 24) -> np.ndarray:
|
||||
"""Sign of the slope of an EMA: ema[i] vs ema[i-slope_win]. -> {-1,0,+1}."""
|
||||
ema = pd.Series(c).ewm(span=span, adjust=False).mean().values
|
||||
n = len(c)
|
||||
out = np.zeros(n)
|
||||
out[slope_win:] = np.sign(ema[slope_win:] - ema[:-slope_win])
|
||||
return out
|
||||
|
||||
|
||||
def sig_donchian_state(c, h, l, n_break: int, n_exit: int) -> np.ndarray:
|
||||
"""Donchian breakout with trailing (channel) stop, returns a stateful {-1,0,+1} series.
|
||||
Long when close[i] > prior n_break high; exit/flip via prior n_exit low channel (trailing).
|
||||
Detection uses prior-window extremes EXCLUDING current bar (shift 1) and close[i] -> honest."""
|
||||
hh = pd.Series(h).rolling(n_break).max().shift(1).values
|
||||
ll = pd.Series(l).rolling(n_break).min().shift(1).values
|
||||
xh = pd.Series(h).rolling(n_exit).max().shift(1).values # trailing exit for shorts
|
||||
xl = pd.Series(l).rolling(n_exit).min().shift(1).values # trailing exit for longs
|
||||
n = len(c)
|
||||
state = np.zeros(n)
|
||||
pos = 0
|
||||
for i in range(n):
|
||||
if not np.isfinite(hh[i]):
|
||||
state[i] = 0
|
||||
continue
|
||||
if pos == 1:
|
||||
if c[i] < xl[i]:
|
||||
pos = 0
|
||||
elif pos == -1:
|
||||
if c[i] > xh[i]:
|
||||
pos = 0
|
||||
if pos == 0:
|
||||
if c[i] > hh[i]:
|
||||
pos = 1
|
||||
elif c[i] < ll[i]:
|
||||
pos = -1
|
||||
state[i] = pos
|
||||
return state
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Position construction (vol-targeting + leverage cap + long/flat option)
|
||||
# ---------------------------------------------------------------------------
|
||||
def build_target(direction: np.ndarray, vol: np.ndarray, target_vol: float,
|
||||
leverage: float, long_only: bool) -> np.ndarray:
|
||||
"""target[i] = direction[i] * (target_vol / vol[i]), clipped to [-leverage, leverage].
|
||||
direction[i] in [-1,1]; vol[i] annualized realized vol (<= close[i]). long_only clips <0 to 0."""
|
||||
d = direction.copy()
|
||||
if long_only:
|
||||
d = np.clip(d, 0, None)
|
||||
scal = np.where((vol > 0) & np.isfinite(vol), target_vol / vol, 0.0)
|
||||
tgt = d * scal
|
||||
tgt = np.clip(tgt, -leverage, leverage)
|
||||
tgt[~np.isfinite(tgt)] = 0.0
|
||||
return tgt
|
||||
|
||||
|
||||
def equity_from_target(target: np.ndarray, r: np.ndarray, fee_side: float):
|
||||
"""Build equity from a target-position series with NO look-ahead.
|
||||
pos held during bar t = target[t-1]; pnl[t] = target[t-1]*r[t]; fee on turnover."""
|
||||
n = len(target)
|
||||
pos_held = np.zeros(n)
|
||||
pos_held[1:] = target[:-1] # held during bar t = decided at close[t-1]
|
||||
gross = pos_held * r
|
||||
turn = np.abs(np.diff(pos_held, prepend=0.0))
|
||||
net = gross - fee_side * turn
|
||||
net[0] = 0.0
|
||||
net = np.clip(net, -0.99, None) # cannot lose more than capital on a bar
|
||||
equity = np.cumprod(1.0 + net)
|
||||
return equity, net
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Metrics
|
||||
# ---------------------------------------------------------------------------
|
||||
def metrics(equity: np.ndarray, net: np.ndarray, ts: pd.Series) -> dict:
|
||||
rr = net[np.isfinite(net)]
|
||||
sharpe = float(np.mean(rr) / np.std(rr) * np.sqrt(BARS_PER_YEAR)) if np.std(rr) > 0 else 0.0
|
||||
peak = np.maximum.accumulate(equity)
|
||||
dd = float(np.max((peak - equity) / peak))
|
||||
span_days = (ts.iloc[-1] - ts.iloc[0]).total_seconds() / 86400
|
||||
years = span_days / 365.25
|
||||
total = equity[-1] / equity[0]
|
||||
cagr = total ** (1 / years) - 1 if years > 0 and total > 0 else -1.0
|
||||
eq_s = pd.Series(equity, index=ts)
|
||||
yearly = {}
|
||||
for y, g in eq_s.groupby(eq_s.index.year):
|
||||
if len(g) > 1 and g.iloc[0] > 0:
|
||||
yearly[int(y)] = float(g.iloc[-1] / g.iloc[0] - 1)
|
||||
daily_2k = (2000 * total - 2000) / span_days if span_days > 0 else 0.0
|
||||
return dict(sharpe=sharpe, max_dd=dd, cagr=cagr, total=total - 1,
|
||||
yearly=yearly, daily_2k=daily_2k, vol_ann=float(np.std(rr) * np.sqrt(BARS_PER_YEAR)))
|
||||
|
||||
|
||||
def avg_gross(target: np.ndarray) -> float:
|
||||
"""Average absolute position = average gross leverage actually deployed."""
|
||||
t = target[np.isfinite(target)]
|
||||
return float(np.mean(np.abs(t))) if len(t) else 0.0
|
||||
|
||||
|
||||
def fmt(m, label):
|
||||
return (f" {label:<34s} ret={m['total']*100:>+9.0f}% CAGR={m['cagr']*100:>+6.1f}% "
|
||||
f"Sh={m['sharpe']:>5.2f} DD={m['max_dd']*100:>4.1f}% volA={m['vol_ann']*100:>4.0f}% "
|
||||
f"€/d(2k)={m['daily_2k']:>+7.2f}")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Strategy assembly
|
||||
# ---------------------------------------------------------------------------
|
||||
def make_direction(df: pd.DataFrame, kind: str, params: dict) -> np.ndarray:
|
||||
c = df["close"].values.astype(float)
|
||||
if kind == "TSMOM":
|
||||
return sig_tsmom_blend(c, params.get("horizons", (H1, H3, H6)))
|
||||
if kind == "MASLOPE":
|
||||
return sig_ma_slope(c, params["span"], params.get("slope_win", 24))
|
||||
if kind == "DONCHIAN":
|
||||
h = df["high"].values.astype(float)
|
||||
l = df["low"].values.astype(float)
|
||||
return sig_donchian_state(c, h, l, params["n_break"], params["n_exit"])
|
||||
raise ValueError(kind)
|
||||
|
||||
|
||||
def run_asset(df, kind, params, target_vol, leverage, long_only, fee_side=FEE_SIDE):
|
||||
c = df["close"].values.astype(float)
|
||||
r = simple_returns(c)
|
||||
vol = realized_vol(r, params.get("vol_win", 30 * 24))
|
||||
direction = make_direction(df, kind, params)
|
||||
tgt = build_target(direction, vol, target_vol, leverage, long_only)
|
||||
equity, net = equity_from_target(tgt, r, fee_side)
|
||||
ts = df["datetime"]
|
||||
m = metrics(equity, net, ts)
|
||||
m["target"] = tgt
|
||||
m["net"] = net
|
||||
m["ts"] = ts
|
||||
m["equity"] = equity
|
||||
return m
|
||||
|
||||
|
||||
def buy_hold(df):
|
||||
c = df["close"].values.astype(float)
|
||||
r = simple_returns(c)
|
||||
equity = np.cumprod(1.0 + np.clip(r, -0.99, None))
|
||||
return metrics(equity, r, df["datetime"])
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Portfolio (equal-weight BTC+ETH, rebalanced each bar on common timestamps)
|
||||
# ---------------------------------------------------------------------------
|
||||
def portfolio(net_btc_df, net_eth_df, w=(0.5, 0.5)):
|
||||
"""Combine two per-bar net-return series aligned on common timestamps."""
|
||||
a = pd.Series(net_btc_df["net"], index=net_btc_df["ts"].values)
|
||||
b = pd.Series(net_eth_df["net"], index=net_eth_df["ts"].values)
|
||||
j = pd.concat([a.rename("a"), b.rename("b")], axis=1, join="inner").fillna(0.0)
|
||||
combo = w[0] * j["a"].values + w[1] * j["b"].values
|
||||
equity = np.cumprod(1.0 + np.clip(combo, -0.99, None))
|
||||
ts = pd.Series(pd.to_datetime(j.index))
|
||||
return metrics(equity, combo, ts)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Reporting helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
ALL_YEARS = list(range(2018, 2027))
|
||||
|
||||
|
||||
def print_yearly_row(label, m):
|
||||
cells = []
|
||||
for y in ALL_YEARS:
|
||||
v = m["yearly"].get(y)
|
||||
cells.append(" . " if v is None else f"{v*100:>+6.0f}%")
|
||||
print(f" {label:<26s} " + " ".join(cells))
|
||||
|
||||
|
||||
def yearly_header():
|
||||
print(f" {'config':<26s} " + " ".join(f"{y:>7d}" for y in ALL_YEARS))
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Experiments
|
||||
# ---------------------------------------------------------------------------
|
||||
def main():
|
||||
pd.set_option("display.width", 220)
|
||||
dfs = {a: load(a, TF) for a in ASSETS}
|
||||
|
||||
print("=" * 130)
|
||||
print("# TRACK D — VOL-TARGETED TREND PORTFOLIO (BTC+ETH, 1h, Deribit certified)")
|
||||
print("# Equity built from target-position series; positions shifted +1 bar (no look-ahead);")
|
||||
print("# fee = 0.05%/side (0.10% RT) on turnover. Vol-targeting scales by inverse realized vol.")
|
||||
print("=" * 130)
|
||||
|
||||
print("\n# BUY & HOLD BENCHMARK (the DD/return bar trend must beat on risk-adjusted basis)")
|
||||
yearly_header()
|
||||
bh = {}
|
||||
for a in ASSETS:
|
||||
bh[a] = buy_hold(dfs[a])
|
||||
print(fmt(bh[a], f"B&H {a}"))
|
||||
print_yearly_row(f"B&H {a} yearly", bh[a])
|
||||
bh_port = portfolio({"net": simple_returns(dfs["BTC"]["close"].values), "ts": dfs["BTC"]["datetime"]},
|
||||
{"net": simple_returns(dfs["ETH"]["close"].values), "ts": dfs["ETH"]["datetime"]})
|
||||
print(fmt(bh_port, "B&H 50/50 BTC+ETH"))
|
||||
print_yearly_row("B&H port yearly", bh_port)
|
||||
|
||||
# ----------------------------------------------------------------------
|
||||
# 1. BROAD SCAN: strategies x vol-target x leverage x long-only, per asset & portfolio
|
||||
# ----------------------------------------------------------------------
|
||||
print("\n" + "=" * 130)
|
||||
print("# 1) BROAD SCAN — per-asset & 50/50 portfolio, vol-target=20%, leverage cap 2x")
|
||||
print("# (TSMOM 1-3-6m blend / MA-slope / Donchian-trailing; long-short vs long-flat)")
|
||||
print("=" * 130)
|
||||
|
||||
strat_defs = [
|
||||
("TSMOM", dict(horizons=(H1, H3, H6), vol_win=30 * 24)),
|
||||
("MASLOPE", dict(span=200, slope_win=48, vol_win=30 * 24)),
|
||||
("DONCHIAN", dict(n_break=200, n_exit=100, vol_win=30 * 24)),
|
||||
]
|
||||
for long_only in (False, True):
|
||||
mode = "LONG-FLAT" if long_only else "LONG-SHORT"
|
||||
print(f"\n --- {mode} ---")
|
||||
for kind, params in strat_defs:
|
||||
sleeves = {}
|
||||
for a in ASSETS:
|
||||
m = run_asset(dfs[a], kind, params, target_vol=0.20, leverage=2.0, long_only=long_only)
|
||||
sleeves[a] = m
|
||||
print(fmt(m, f"{kind} {a}"))
|
||||
port = portfolio(sleeves["BTC"], sleeves["ETH"])
|
||||
print(fmt(port, f"{kind} PORTFOLIO 50/50"))
|
||||
print_yearly_row(f"{kind} port yearly", port)
|
||||
|
||||
# ----------------------------------------------------------------------
|
||||
# 2. GRID ROBUSTNESS on the portfolio: vol-target x leverage x vol-window
|
||||
# using the multi-horizon TSMOM blend (the most diversified trend signal)
|
||||
# ----------------------------------------------------------------------
|
||||
print("\n" + "=" * 130)
|
||||
print("# 2) GRID ROBUSTNESS — TSMOM 1-3-6m blend, 50/50 portfolio (LONG-SHORT)")
|
||||
print("# Sweep target-vol x leverage-cap. A real config is positive across the neighborhood.")
|
||||
print("=" * 130)
|
||||
hdr = " " + "tvol\\lev".ljust(8) + "".join(f"{lev:.0f}x".rjust(26) for lev in (1.0, 1.5, 2.0, 3.0))
|
||||
print(hdr)
|
||||
grid = {}
|
||||
for tvol in (0.10, 0.15, 0.20, 0.30, 0.40):
|
||||
row = f" {tvol*100:>6.0f}% "
|
||||
for lev in (1.0, 1.5, 2.0, 3.0):
|
||||
sleeves = {}
|
||||
for a in ASSETS:
|
||||
sleeves[a] = run_asset(dfs[a], "TSMOM",
|
||||
dict(horizons=(H1, H3, H6), vol_win=30 * 24),
|
||||
target_vol=tvol, leverage=lev, long_only=False)
|
||||
port = portfolio(sleeves["BTC"], sleeves["ETH"])
|
||||
grid[(tvol, lev)] = port
|
||||
row += f" Sh{port['sharpe']:>4.2f} DD{port['max_dd']*100:>3.0f} C{port['cagr']*100:>+4.0f}"
|
||||
print(row)
|
||||
|
||||
# ----------------------------------------------------------------------
|
||||
# 3. HORIZON-SET robustness (is the 1-3-6m blend a plateau or a lucky combo?)
|
||||
# ----------------------------------------------------------------------
|
||||
print("\n" + "=" * 130)
|
||||
print("# 3) HORIZON-SET ROBUSTNESS — TSMOM blend, portfolio, tvol=20% lev=2x (LONG-SHORT)")
|
||||
print("=" * 130)
|
||||
horizon_sets = {
|
||||
"1m only": (H1,), "3m only": (H3,), "6m only": (H6,),
|
||||
"1-3m": (H1, H3), "3-6m": (H3, H6), "1-3-6m": (H1, H3, H6),
|
||||
"1-2-4m": (30 * 24, 60 * 24, 120 * 24), "2-4-8m": (60 * 24, 120 * 24, 240 * 24),
|
||||
}
|
||||
yearly_header()
|
||||
for name, hs in horizon_sets.items():
|
||||
sleeves = {a: run_asset(dfs[a], "TSMOM", dict(horizons=hs, vol_win=30 * 24),
|
||||
target_vol=0.20, leverage=2.0, long_only=False) for a in ASSETS}
|
||||
port = portfolio(sleeves["BTC"], sleeves["ETH"])
|
||||
print(fmt(port, f"TSMOM {name}"))
|
||||
print()
|
||||
for name, hs in horizon_sets.items():
|
||||
sleeves = {a: run_asset(dfs[a], "TSMOM", dict(horizons=hs, vol_win=30 * 24),
|
||||
target_vol=0.20, leverage=2.0, long_only=False) for a in ASSETS}
|
||||
port = portfolio(sleeves["BTC"], sleeves["ETH"])
|
||||
print_yearly_row(f"{name}", port)
|
||||
|
||||
# ----------------------------------------------------------------------
|
||||
# 4. WALK-FORWARD: EARLY (<=2021) tune / LATE (>=2022) confirm
|
||||
# Same single param set for BOTH assets; we just split the equity by date.
|
||||
# ----------------------------------------------------------------------
|
||||
print("\n" + "=" * 130)
|
||||
print("# 4) WALK-FORWARD — split portfolio equity into EARLY (2018-2021) vs LATE (2022-2026)")
|
||||
print("# One param set, both assets. Both halves must earn for the edge to be regime-robust.")
|
||||
print("=" * 130)
|
||||
cfg = dict(kind="TSMOM", params=dict(horizons=(H1, H3, H6), vol_win=30 * 24),
|
||||
target_vol=0.20, leverage=2.0, long_only=False)
|
||||
sleeves = {a: run_asset(dfs[a], cfg["kind"], cfg["params"], cfg["target_vol"],
|
||||
cfg["leverage"], cfg["long_only"]) for a in ASSETS}
|
||||
a = pd.Series(sleeves["BTC"]["net"], index=sleeves["BTC"]["ts"].values)
|
||||
b = pd.Series(sleeves["ETH"]["net"], index=sleeves["ETH"]["ts"].values)
|
||||
j = pd.concat([a.rename("a"), b.rename("b")], axis=1, join="inner").fillna(0.0)
|
||||
combo = 0.5 * j["a"].values + 0.5 * j["b"].values
|
||||
idx = pd.to_datetime(j.index)
|
||||
for lab, mask in (("EARLY 2018-2021", idx.year <= 2021), ("LATE 2022-2026", idx.year >= 2022)):
|
||||
sub = combo[mask]
|
||||
eq = np.cumprod(1.0 + np.clip(sub, -0.99, None))
|
||||
m = metrics(eq, sub, pd.Series(idx[mask]))
|
||||
print(fmt(m, lab))
|
||||
print_yearly_row(f"{lab} yearly", m)
|
||||
|
||||
# ----------------------------------------------------------------------
|
||||
# 5. FEE & LEVERAGE SWEEP on the headline portfolio config
|
||||
# ----------------------------------------------------------------------
|
||||
print("\n" + "=" * 130)
|
||||
print("# 5) FEE & LEVERAGE SWEEP — TSMOM 1-3-6m blend portfolio, tvol=20%")
|
||||
print("=" * 130)
|
||||
print(" fee sweep (leverage cap 2x):")
|
||||
for fee in (0.0005, 0.00075, 0.001, 0.0015, 0.002):
|
||||
sleeves = {a: run_asset(dfs[a], "TSMOM", dict(horizons=(H1, H3, H6), vol_win=30 * 24),
|
||||
target_vol=0.20, leverage=2.0, long_only=False, fee_side=fee)
|
||||
for a in ASSETS}
|
||||
port = portfolio(sleeves["BTC"], sleeves["ETH"])
|
||||
print(fmt(port, f"fee/side={fee:.5f} (RT={2*fee*100:.2f}%)"))
|
||||
print(" leverage sweep (fee 0.05%/side):")
|
||||
for lev in (1.0, 1.5, 2.0, 2.5, 3.0):
|
||||
sleeves = {a: run_asset(dfs[a], "TSMOM", dict(horizons=(H1, H3, H6), vol_win=30 * 24),
|
||||
target_vol=0.20, leverage=lev, long_only=False)
|
||||
for a in ASSETS}
|
||||
port = portfolio(sleeves["BTC"], sleeves["ETH"])
|
||||
print(fmt(port, f"leverage cap={lev:.1f}x"))
|
||||
|
||||
# ----------------------------------------------------------------------
|
||||
# 6. HEADLINE ROBUST CONFIG — full per-year table + sleeves + portfolio
|
||||
# ----------------------------------------------------------------------
|
||||
print("\n" + "=" * 130)
|
||||
print("# 6) HEADLINE ROBUST CONFIG: TSMOM 1-3-6m blend, vol-target 20%, leverage cap 2x, LONG-SHORT")
|
||||
print("=" * 130)
|
||||
yearly_header()
|
||||
sleeves = {a: run_asset(dfs[a], "TSMOM", dict(horizons=(H1, H3, H6), vol_win=30 * 24),
|
||||
target_vol=0.20, leverage=2.0, long_only=False) for a in ASSETS}
|
||||
for a in ASSETS:
|
||||
print(fmt(sleeves[a], f"sleeve {a}"))
|
||||
print_yearly_row(f"sleeve {a} yearly", sleeves[a])
|
||||
port = portfolio(sleeves["BTC"], sleeves["ETH"])
|
||||
print(fmt(port, "PORTFOLIO 50/50"))
|
||||
print_yearly_row("PORTFOLIO yearly", port)
|
||||
|
||||
# also long-flat headline (deployable variant — no shorts/funding complexity)
|
||||
print()
|
||||
sleeves_lf = {a: run_asset(dfs[a], "TSMOM", dict(horizons=(H1, H3, H6), vol_win=30 * 24),
|
||||
target_vol=0.20, leverage=2.0, long_only=True) for a in ASSETS}
|
||||
port_lf = portfolio(sleeves_lf["BTC"], sleeves_lf["ETH"])
|
||||
print(fmt(port_lf, "PORTFOLIO 50/50 LONG-FLAT"))
|
||||
print_yearly_row("PORTFOLIO LF yearly", port_lf)
|
||||
|
||||
# ----------------------------------------------------------------------
|
||||
# 7. €/DAY ON 2000 — what leverage gets us toward 50/day, and the DD it costs
|
||||
# ----------------------------------------------------------------------
|
||||
print("\n" + "=" * 130)
|
||||
print("# 7) PATH TO ~50 EUR/day on 2000 — the REAL lever is TARGET-VOL, not the leverage cap.")
|
||||
print("# At tvol=20%% on 60-80%% crypto vol, positions stay sub-1x: the leverage cap NEVER binds.")
|
||||
print("# To deploy real leverage you raise target-vol; Sharpe is ~constant, DD scales ~linearly.")
|
||||
print("# 'avg gross' = mean |position| = leverage actually used. (cap fixed at 3x here)")
|
||||
print("=" * 130)
|
||||
print(f" {'target_vol':<12s}{'avgGross':>10s}{'CAGR':>9s}{'Sharpe':>9s}{'maxDD':>8s}"
|
||||
f"{'€/day(2k,avg)':>16s}{'final/2k':>12s}")
|
||||
for tvol in (0.20, 0.40, 0.60, 0.80, 1.00):
|
||||
sleeves = {a: run_asset(dfs[a], "TSMOM", dict(horizons=(H1, H3, H6), vol_win=30 * 24),
|
||||
target_vol=tvol, leverage=3.0, long_only=False) for a in ASSETS}
|
||||
port = portfolio(sleeves["BTC"], sleeves["ETH"])
|
||||
ag = 0.5 * (avg_gross(sleeves["BTC"]["target"]) + avg_gross(sleeves["ETH"]["target"]))
|
||||
print(f" {tvol*100:>8.0f}% {ag:>9.2f}x{port['cagr']*100:>+8.1f}%{port['sharpe']:>9.2f}"
|
||||
f"{port['max_dd']*100:>7.1f}%{port['daily_2k']:>+16.2f}{(1+port['total']):>12.1f}x")
|
||||
# steady-state €/day at current capital under headline CAGR
|
||||
print("\n Steady-state €/day implied by headline CAGR (NOT path-dependent), at various capital:")
|
||||
sleeves = {a: run_asset(dfs[a], "TSMOM", dict(horizons=(H1, H3, H6), vol_win=30 * 24),
|
||||
target_vol=0.20, leverage=2.0, long_only=False) for a in ASSETS}
|
||||
port = portfolio(sleeves["BTC"], sleeves["ETH"])
|
||||
g = port["cagr"]
|
||||
daily_rate = (1 + g) ** (1 / 365.25) - 1
|
||||
for cap in (2000, 5000, 10000, 50000, 100000):
|
||||
print(f" capital={cap:>7d} ~€/day = {cap*daily_rate:>+8.2f} (CAGR={g*100:+.1f}%)")
|
||||
need = 50.0 / daily_rate if daily_rate > 0 else float("inf")
|
||||
print(f"\n To average ~50 EUR/day at this CAGR you'd need ~{need:,.0f} capital "
|
||||
f"(at leverage 2x, maxDD~{port['max_dd']*100:.0f}%).")
|
||||
|
||||
print("\nDONE. See the report/diary for the honest verdict.")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,526 @@
|
||||
"""TRACK E — CROSS-SECTIONAL BTC↔ETH relative-value + ENSEMBLE synthesis.
|
||||
|
||||
Two parts, both on certified Deribit-mainnet data (only BTC/ETH), both honest:
|
||||
|
||||
PART 1 — RELATIVE VALUE (market-neutral-ish spread trading on TWO assets):
|
||||
* XS relative momentum: go long the stronger asset, short the weaker (dollar-neutral).
|
||||
* ETH/BTC ratio TREND (z-momentum) and ratio MEAN-REVERSION (z-fade of log-ratio).
|
||||
* Lead-lag (descriptive): does BTC's last-bar move predict ETH's next bar (and vice versa)?
|
||||
All positions are decided with data <= close[i] and HELD over the NEXT bar (i->i+1):
|
||||
realized PnL on bar k uses position set at k-1 -> strict 1-bar shift, NO look-ahead.
|
||||
Fees are turnover-based: |Δpos| * fee_rt/2 PER LEG (a +1↔-1 flip = one round trip = fee_rt).
|
||||
|
||||
PART 2 — ENSEMBLE:
|
||||
Combine the genuinely-positive residual sleeves into ONE portfolio equity curve:
|
||||
(S1) BTC low-turnover ML momentum (trackB best honest cell: W16000 H24 thr0.10, 1h)
|
||||
(S2) Trend-1h, the only cross-asset-robust trend cell from trackA (Donchian N=200 H=12)
|
||||
(S3) the best relative-value sleeve found in PART 1 (if any net-positive OOS)
|
||||
Report combined Sharpe / maxDD / CAGR / EUR-per-day-on-2000 AND the sleeve correlation
|
||||
matrix. A real ensemble edge must be net-positive OOS and LOWER drawdown than its parts.
|
||||
|
||||
Run: uv run python scripts/research/trackE_xsec_ensemble.py
|
||||
uv run python scripts/research/trackE_xsec_ensemble.py --quick (skip slow ML sleeve)
|
||||
uv run python scripts/research/trackE_xsec_ensemble.py --no-cache (recompute ML proba)
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import sys
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
|
||||
|
||||
from src.backtest.harness import load, backtest_signals, oos_split
|
||||
# reuse trackB ML machinery (strict walk-forward, no leakage) and trackA donchian
|
||||
from scripts.research.trackB_ml import (
|
||||
build_features, forward_labels, walk_forward_proba, proba_to_entries, mask_entries,
|
||||
RETRAIN_K,
|
||||
)
|
||||
from scripts.research.trackA_trend import sig_donchian
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
|
||||
FEE = 0.001 # 0.10% round-trip baseline (per leg for the pair)
|
||||
BARS_PER_YEAR_1H = 24 * 365.25
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# Generic honest stats on a per-bar RETURN series (returns realized bar (k-1)->k)
|
||||
# ===========================================================================
|
||||
def equity_from_returns(rets: np.ndarray) -> np.ndarray:
|
||||
eq = np.cumprod(1.0 + np.nan_to_num(rets))
|
||||
return eq
|
||||
|
||||
|
||||
def sharpe(rets: np.ndarray, bpy: float = BARS_PER_YEAR_1H) -> float:
|
||||
r = rets[np.isfinite(rets)]
|
||||
if len(r) < 3 or np.std(r) == 0:
|
||||
return 0.0
|
||||
return float(np.mean(r) / np.std(r) * np.sqrt(bpy))
|
||||
|
||||
|
||||
def max_dd(equity: np.ndarray) -> float:
|
||||
peak = np.maximum.accumulate(equity)
|
||||
dd = (peak - equity) / peak
|
||||
return float(np.max(dd)) if len(dd) else 0.0
|
||||
|
||||
|
||||
def cagr(equity: np.ndarray, ts: pd.Series) -> float:
|
||||
if len(equity) < 2:
|
||||
return 0.0
|
||||
days = (ts.iloc[-1] - ts.iloc[0]).total_seconds() / 86400
|
||||
years = days / 365.25 if days > 0 else 1.0
|
||||
if years <= 0 or equity[-1] <= 0:
|
||||
return -1.0
|
||||
return float(equity[-1] ** (1 / years) - 1)
|
||||
|
||||
|
||||
def daily_profit(equity: np.ndarray, ts: pd.Series, capital: float = 2000.0) -> float:
|
||||
if len(equity) < 2:
|
||||
return 0.0
|
||||
days = (ts.iloc[-1] - ts.iloc[0]).total_seconds() / 86400
|
||||
if days <= 0:
|
||||
return 0.0
|
||||
final = capital * equity[-1] / equity[0]
|
||||
return (final - capital) / days
|
||||
|
||||
|
||||
def yearly_returns(rets: np.ndarray, ts: pd.Series) -> dict:
|
||||
eq = equity_from_returns(rets)
|
||||
s = pd.Series(eq, index=pd.DatetimeIndex(ts))
|
||||
out = {}
|
||||
for y, g in s.groupby(s.index.year):
|
||||
if len(g) > 1 and g.iloc[0] > 0:
|
||||
out[int(y)] = float(g.iloc[-1] / g.iloc[0] - 1)
|
||||
return out
|
||||
|
||||
|
||||
def stat_block(rets: np.ndarray, ts: pd.Series, bpy: float = BARS_PER_YEAR_1H) -> dict:
|
||||
eq = equity_from_returns(rets)
|
||||
return dict(
|
||||
net=float(eq[-1] - 1.0), sharpe=sharpe(rets, bpy), max_dd=max_dd(eq),
|
||||
cagr=cagr(eq, ts), eur_day=daily_profit(eq, ts), equity=eq,
|
||||
turnover=float(np.mean(np.abs(np.diff(np.sign(rets) != 0)))), # placeholder, unused
|
||||
)
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# RELATIVE-VALUE ENGINE — two legs, turnover-based fees, strict 1-bar shift.
|
||||
# pos arrays are decided at close[i] (data<=i). Realized return on bar k uses pos[k-1].
|
||||
# ===========================================================================
|
||||
def pair_returns(cB: np.ndarray, cE: np.ndarray, posB: np.ndarray, posE: np.ndarray,
|
||||
fee_rt: float = FEE) -> np.ndarray:
|
||||
"""Per-bar net return series for a two-leg book. rets[k] realized on bar (k-1)->k.
|
||||
Fee = (|ΔposB| + |ΔposE|) * fee_rt/2 charged when the position is (re)set."""
|
||||
n = len(cB)
|
||||
aretB = np.zeros(n); aretE = np.zeros(n)
|
||||
aretB[1:] = cB[1:] / cB[:-1] - 1.0
|
||||
aretE[1:] = cE[1:] / cE[:-1] - 1.0
|
||||
rets = np.zeros(n)
|
||||
for k in range(1, n):
|
||||
gross = posB[k - 1] * aretB[k] + posE[k - 1] * aretE[k]
|
||||
pBp = posB[k - 2] if k >= 2 else 0.0
|
||||
pEp = posE[k - 2] if k >= 2 else 0.0
|
||||
turn = abs(posB[k - 1] - pBp) + abs(posE[k - 1] - pEp)
|
||||
rets[k] = gross - turn * fee_rt / 2.0
|
||||
return rets
|
||||
|
||||
|
||||
# --- signal builders: return (posB, posE) arrays, leg notional `leg` (gross = 2*leg) ---
|
||||
def xs_momentum(cB, cE, N, hold, leg=0.5):
|
||||
"""Cross-sectional momentum: long the asset with higher N-bar return, short the other."""
|
||||
n = len(cB)
|
||||
posB = np.zeros(n); posE = np.zeros(n)
|
||||
curB = curE = 0.0
|
||||
for i in range(n):
|
||||
if i >= N and (i % hold == 0):
|
||||
mB = cB[i] / cB[i - N] - 1.0
|
||||
mE = cE[i] / cE[i - N] - 1.0
|
||||
d = 1 if mB > mE else -1 # +1 => BTC stronger -> long BTC short ETH
|
||||
curB = leg * d; curE = -leg * d
|
||||
posB[i] = curB; posE[i] = curE
|
||||
return posB, posE
|
||||
|
||||
|
||||
def ratio_trend(cB, cE, N, hold, leg=0.5):
|
||||
"""Trend on ETH/BTC ratio: ratio rising over N bars -> long ratio (long ETH, short BTC)."""
|
||||
ratio = cE / cB
|
||||
n = len(cB)
|
||||
posB = np.zeros(n); posE = np.zeros(n)
|
||||
curB = curE = 0.0
|
||||
for i in range(n):
|
||||
if i >= N and (i % hold == 0):
|
||||
d = 1 if ratio[i] > ratio[i - N] else -1 # +1 => ratio up -> long ratio
|
||||
curE = leg * d; curB = -leg * d
|
||||
posB[i] = curB; posE[i] = curE
|
||||
return posB, posE
|
||||
|
||||
|
||||
def ratio_meanrev(cB, cE, lookback, z_in, z_exit, max_bars, leg=0.5):
|
||||
"""Mean-reversion (z-fade) on log(ETH/BTC). z>+z_in -> short ratio; z<-z_in -> long ratio.
|
||||
Exit when |z|<z_exit (reverted to mean) or after max_bars. Stateful, honest at close[i]."""
|
||||
logr = np.log(cE / cB)
|
||||
s = pd.Series(logr)
|
||||
ma = s.rolling(lookback).mean().values
|
||||
sd = s.rolling(lookback).std(ddof=0).values
|
||||
z = (logr - ma) / sd
|
||||
n = len(cB)
|
||||
posB = np.zeros(n); posE = np.zeros(n)
|
||||
state = 0 # +1 long ratio, -1 short ratio, 0 flat
|
||||
bars_in = 0
|
||||
for i in range(n):
|
||||
if not np.isfinite(z[i]):
|
||||
posB[i] = 0.0; posE[i] = 0.0; continue
|
||||
if state == 0:
|
||||
if z[i] >= z_in:
|
||||
state = -1; bars_in = 0 # ratio too high -> short ratio
|
||||
elif z[i] <= -z_in:
|
||||
state = 1; bars_in = 0 # ratio too low -> long ratio
|
||||
else:
|
||||
bars_in += 1
|
||||
if abs(z[i]) <= z_exit or bars_in >= max_bars or (state == 1 and z[i] >= z_in) \
|
||||
or (state == -1 and z[i] <= -z_in):
|
||||
state = 0
|
||||
posE[i] = leg * state; posB[i] = -leg * state
|
||||
return posB, posE
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# OOS / fee-sweep helpers for the relative-value sleeves
|
||||
# ===========================================================================
|
||||
def rv_eval(cB, cE, ts, build_fn, params, fee_rt=FEE, frac=0.65):
|
||||
posB, posE = build_fn(cB, cE, **params)
|
||||
rets = pair_returns(cB, cE, posB, posE, fee_rt=fee_rt)
|
||||
cut = int(len(cB) * frac)
|
||||
full = stat_block(rets, ts)
|
||||
is_ = stat_block(rets[:cut], ts.iloc[:cut])
|
||||
oos = stat_block(rets[cut:], ts.iloc[cut:])
|
||||
# turnover: average per-bar leg turnover (both legs)
|
||||
turn = (np.abs(np.diff(posB, prepend=0)) + np.abs(np.diff(posE, prepend=0)))
|
||||
tstats = dict(rets=rets, posB=posB, posE=posE,
|
||||
trades=int((turn > 1e-9).sum()), avg_turn=float(turn.mean()))
|
||||
return full, is_, oos, tstats
|
||||
|
||||
|
||||
def fmt(s):
|
||||
return (f"net={s['net']*100:>+8.0f}% Sh={s['sharpe']:>+5.2f} DD={s['max_dd']*100:>4.0f}% "
|
||||
f"CAGR={s['cagr']*100:>+6.1f}% €/d={s['eur_day']:>+6.2f}")
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# PART 1
|
||||
# ===========================================================================
|
||||
def part1_relative_value(quick=False):
|
||||
print("=" * 104)
|
||||
print("PART 1 — CROSS-SECTIONAL / RELATIVE-VALUE (BTC↔ETH, 1h, market-neutral spread)")
|
||||
print("=" * 104)
|
||||
b = load("BTC", "1h"); e = load("ETH", "1h")
|
||||
m = pd.merge(b[["timestamp", "close"]], e[["timestamp", "close"]],
|
||||
on="timestamp", suffixes=("_b", "_e")).reset_index(drop=True)
|
||||
ts = pd.to_datetime(m["timestamp"], unit="ms", utc=True)
|
||||
cB = m["close_b"].to_numpy(float); cE = m["close_e"].to_numpy(float)
|
||||
cut = int(len(m) * 0.65)
|
||||
print(f" common 1h bars: {len(m)} {ts.iloc[0].date()} → {ts.iloc[-1].date()} "
|
||||
f"(OOS starts {ts.iloc[cut].date()})")
|
||||
rb = np.log(cB[1:] / cB[:-1]); re = np.log(cE[1:] / cE[:-1])
|
||||
print(f" contemporaneous corr(BTC,ETH 1h logret) = {np.corrcoef(rb, re)[0,1]:.3f} "
|
||||
f"(very high → the only tradable structure is the SPREAD)")
|
||||
|
||||
# ---- LEAD-LAG (descriptive, both directions, IS vs OOS) ----
|
||||
print("\n -- LEAD-LAG (descriptive: does last-bar move of X predict next bar of Y?) --")
|
||||
def ll(a_prev, b_next):
|
||||
a = a_prev[np.isfinite(a_prev) & np.isfinite(b_next)]
|
||||
bb = b_next[np.isfinite(a_prev) & np.isfinite(b_next)]
|
||||
return np.corrcoef(a, bb)[0, 1] if len(a) > 30 else np.nan
|
||||
print(f" corr(rB[i], rE[i+1]) = {ll(rb[:-1], re[1:]):+.4f} "
|
||||
f"corr(rE[i], rB[i+1]) = {ll(re[:-1], rb[1:]):+.4f}")
|
||||
print(f" corr(rB[i], rB[i+1]) = {ll(rb[:-1], rb[1:]):+.4f} "
|
||||
f"corr(rE[i], rE[i+1]) = {ll(re[:-1], re[1:]):+.4f}")
|
||||
print(" → |lead-lag| ~0.01-0.02: NO exploitable cross-predictive edge. Not pursued as a sleeve.")
|
||||
|
||||
results = {}
|
||||
|
||||
# ---- A) XS relative momentum grid ----
|
||||
print("\n -- (A) XS RELATIVE MOMENTUM: long stronger / short weaker (dollar-neutral, gross=1) --")
|
||||
print(" param FULL | OOS")
|
||||
Ns = [24, 72, 168, 336] if not quick else [72, 168]
|
||||
holds = [6, 24, 72] if not quick else [24, 72]
|
||||
best_xs = None
|
||||
for N in Ns:
|
||||
for hold in holds:
|
||||
full, is_, oos, tstat = rv_eval(cB, cE, ts, xs_momentum, dict(N=N, hold=hold))
|
||||
ok = oos["net"] > 0 and oos["sharpe"] > 0
|
||||
print(f" N={N:>3} hold={hold:>2} | {fmt(full)} | OOS {fmt(oos)} "
|
||||
f"tr={tstat['trades']:>4} {'OK' if ok else ''}")
|
||||
if oos["net"] > 0 and (best_xs is None or oos["sharpe"] > best_xs[2]["sharpe"]):
|
||||
best_xs = (dict(N=N, hold=hold), full, oos, tstat, "xs_momentum")
|
||||
results["xs_momentum"] = best_xs
|
||||
|
||||
# ---- B) ETH/BTC ratio TREND grid ----
|
||||
print("\n -- (B) ETH/BTC RATIO TREND: long ratio when rising over N (long ETH/short BTC) --")
|
||||
print(" NOTE: with only TWO assets this is ALGEBRAICALLY IDENTICAL to (A) — 'long the")
|
||||
print(" stronger' ≡ 'trade the ratio trend'. Shown separately only to make that explicit.")
|
||||
best_rt = None
|
||||
for N in Ns:
|
||||
for hold in holds:
|
||||
full, is_, oos, tstat = rv_eval(cB, cE, ts, ratio_trend, dict(N=N, hold=hold))
|
||||
ok = oos["net"] > 0 and oos["sharpe"] > 0
|
||||
print(f" N={N:>3} hold={hold:>2} | {fmt(full)} | OOS {fmt(oos)} "
|
||||
f"tr={tstat['trades']:>4} {'OK' if ok else ''}")
|
||||
if oos["net"] > 0 and (best_rt is None or oos["sharpe"] > best_rt[2]["sharpe"]):
|
||||
best_rt = (dict(N=N, hold=hold), full, oos, tstat, "ratio_trend")
|
||||
results["ratio_trend"] = best_rt
|
||||
|
||||
# ---- C) ETH/BTC ratio MEAN-REVERSION grid ----
|
||||
print("\n -- (C) ETH/BTC RATIO MEAN-REVERSION: z-fade of log(ETH/BTC) --")
|
||||
best_mr = None
|
||||
LBs = [48, 168, 336] if not quick else [168]
|
||||
zins = [1.5, 2.0, 2.5] if not quick else [2.0]
|
||||
for lb in LBs:
|
||||
for zin in zins:
|
||||
full, is_, oos, tstat = rv_eval(cB, cE, ts, ratio_meanrev,
|
||||
dict(lookback=lb, z_in=zin, z_exit=0.5, max_bars=72))
|
||||
ok = oos["net"] > 0 and oos["sharpe"] > 0
|
||||
print(f" lb={lb:>3} zin={zin} | {fmt(full)} | OOS {fmt(oos)} "
|
||||
f"tr={tstat['trades']:>4} {'OK' if ok else ''}")
|
||||
if oos["net"] > 0 and (best_mr is None or oos["sharpe"] > best_mr[2]["sharpe"]):
|
||||
best_mr = (dict(lookback=lb, z_in=zin, z_exit=0.5, max_bars=72),
|
||||
full, oos, tstat, "ratio_meanrev")
|
||||
results["ratio_meanrev"] = best_mr
|
||||
|
||||
# ---- choose the single best RV sleeve (positive OOS, highest OOS Sharpe) ----
|
||||
cands = [v for v in results.values() if v is not None]
|
||||
cands.sort(key=lambda v: v[2]["sharpe"], reverse=True)
|
||||
best = cands[0] if cands else None
|
||||
|
||||
print("\n -- RELATIVE-VALUE SUMMARY (best per family that is OOS net-positive) --")
|
||||
for fam in ("xs_momentum", "ratio_trend", "ratio_meanrev"):
|
||||
v = results[fam]
|
||||
if v is None:
|
||||
print(f" {fam:<14}: no OOS net-positive cell.")
|
||||
else:
|
||||
params, full, oos, tstat, _ = v
|
||||
print(f" {fam:<14}: {params} FULL {fmt(full)} | OOS {fmt(oos)}")
|
||||
|
||||
if best is None:
|
||||
print("\n >> NO relative-value sleeve is OOS net-positive. No RV edge to add to the ensemble.")
|
||||
return None, (cB, cE, ts)
|
||||
|
||||
params, full, oos, tstat, fam = best
|
||||
print(f"\n >> BEST RV sleeve: {fam} {params} (OOS Sharpe {oos['sharpe']:+.2f})")
|
||||
|
||||
# ---- per-year + fee sweep + grid-neighbourhood robustness on the winner ----
|
||||
build_fn = {"xs_momentum": xs_momentum, "ratio_trend": ratio_trend,
|
||||
"ratio_meanrev": ratio_meanrev}[fam]
|
||||
fullr, _, _, _ = rv_eval(cB, cE, ts, build_fn, params)
|
||||
print("\n per-year (full):")
|
||||
yr = yearly_returns(fullr["rets"] if False else pair_returns(cB, cE,
|
||||
*build_fn(cB, cE, **params)), ts)
|
||||
for y in sorted(yr):
|
||||
print(f" {y}: {yr[y]*100:>+7.1f}%")
|
||||
print("\n fee sweep (full-sample net, baseline 0.10% RT/leg):")
|
||||
for f in (0.0, 0.0005, 0.001, 0.0015, 0.002):
|
||||
fr, _, fo, _ = rv_eval(cB, cE, ts, build_fn, params, fee_rt=f)
|
||||
print(f" fee={f*1000:.1f}bp/leg → FULL net={fr['net']*100:>+7.0f}% "
|
||||
f"OOS net={fo['net']*100:>+7.0f}% (Sh {fo['sharpe']:+.2f})")
|
||||
|
||||
return best, (cB, cE, ts)
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# PART 2 — ENSEMBLE
|
||||
# ===========================================================================
|
||||
def lr_factory():
|
||||
return LogisticRegression(C=1.0, max_iter=300, class_weight="balanced")
|
||||
|
||||
|
||||
def ml_sleeve_btc(cache=True, no_cache=False):
|
||||
"""BTC low-turnover ML momentum sleeve (trackB best honest cell W16000 H24 thr0.10)."""
|
||||
W, H, thr = 16000, 24, 0.10
|
||||
df = load("BTC", "1h")
|
||||
cpath = Path(__file__).resolve().parent / ".cache_trackE_btc_ml_proba.npy"
|
||||
proba = None
|
||||
if cache and not no_cache and cpath.exists():
|
||||
arr = np.load(cpath)
|
||||
if len(arr) == len(df):
|
||||
proba = arr
|
||||
print(f" [S1 ML] loaded cached proba ({cpath.name})")
|
||||
if proba is None:
|
||||
print(f" [S1 ML] walk-forward LogisticRegression W{W} H{H} (slow ~1-2min)...")
|
||||
t0 = time.time()
|
||||
X, names, fvalid = build_features(df)
|
||||
warmup = int(np.argmax(fvalid)) if fvalid.any() else 0
|
||||
y, _fwd, lvalid = forward_labels(df, H)
|
||||
proba = walk_forward_proba(X, y, fvalid, lvalid, warmup, W, H, RETRAIN_K, lr_factory)
|
||||
np.save(cpath, proba)
|
||||
print(f" [S1 ML] done ({time.time()-t0:.0f}s), cached.")
|
||||
n = len(df)
|
||||
entries = proba_to_entries(proba, thr, H, n)
|
||||
m = backtest_signals(df, entries, fee_rt=FEE, asset="BTC", tf="1h")
|
||||
return m, df, f"BTC-ML W{W}H{H}thr{thr}"
|
||||
|
||||
|
||||
def trend_sleeve_btc():
|
||||
"""Trend-1h sleeve: Donchian N=200 H=12 on BTC (the only cross-asset-robust trend cell)."""
|
||||
df = load("BTC", "1h")
|
||||
entries = sig_donchian(df, lookback=200, hold=12)
|
||||
m = backtest_signals(df, entries, fee_rt=FEE, asset="BTC", tf="1h")
|
||||
return m, df, "BTC-Trend Donchian200/12"
|
||||
|
||||
|
||||
def metrics_to_returns(m):
|
||||
"""Per-bar return series from a harness Metrics equity, indexed by its timestamps."""
|
||||
eq = m.equity.astype(float)
|
||||
ts = m.eq_index
|
||||
rets = np.zeros(len(eq))
|
||||
rets[1:] = eq[1:] / np.where(eq[:-1] == 0, np.nan, eq[:-1]) - 1.0
|
||||
rets = np.nan_to_num(rets)
|
||||
return pd.Series(rets, index=pd.DatetimeIndex(ts))
|
||||
|
||||
|
||||
def part2_ensemble(rv_best, rv_data, quick=False, no_cache=False):
|
||||
print("\n" + "=" * 104)
|
||||
print("PART 2 — ENSEMBLE (combine weakly-correlated residual sleeves into one portfolio)")
|
||||
print("=" * 104)
|
||||
|
||||
sleeves = {} # name -> pd.Series of per-bar returns indexed by ts
|
||||
|
||||
# S2 trend (fast, always)
|
||||
mt, dft, tname = trend_sleeve_btc()
|
||||
sleeves["S2_trend"] = metrics_to_returns(mt)
|
||||
print(f" [S2] {tname:<28} net={mt.net_return*100:>+7.0f}% Sh={mt.sharpe:+.2f} "
|
||||
f"DD={mt.max_dd*100:.0f}% €/d={mt.daily_profit(2000):+.2f}")
|
||||
|
||||
# S3 relative value (from PART 1)
|
||||
if rv_best is not None:
|
||||
params, full, oos, tstat, fam = rv_best
|
||||
cB, cE, ts = rv_data
|
||||
build_fn = {"xs_momentum": xs_momentum, "ratio_trend": ratio_trend,
|
||||
"ratio_meanrev": ratio_meanrev}[fam]
|
||||
posB, posE = build_fn(cB, cE, **params)
|
||||
rv_rets = pair_returns(cB, cE, posB, posE, fee_rt=FEE)
|
||||
sleeves["S3_relval"] = pd.Series(rv_rets, index=pd.DatetimeIndex(ts))
|
||||
print(f" [S3] RV {fam} {params} net={full['net']*100:>+7.0f}% "
|
||||
f"Sh={full['sharpe']:+.2f} DD={full['max_dd']*100:.0f}% €/d={full['eur_day']:+.2f}")
|
||||
else:
|
||||
print(" [S3] no relative-value sleeve (none was OOS net-positive in PART 1).")
|
||||
|
||||
# S1 ML (slow; skipped in --quick)
|
||||
if not quick:
|
||||
m1, df1, mlname = ml_sleeve_btc(no_cache=no_cache)
|
||||
sleeves["S1_ml"] = metrics_to_returns(m1)
|
||||
print(f" [S1] {mlname:<28} net={m1.net_return*100:>+7.0f}% Sh={m1.sharpe:+.2f} "
|
||||
f"DD={m1.max_dd*100:.0f}% €/d={m1.daily_profit(2000):+.2f}")
|
||||
else:
|
||||
print(" [S1] ML sleeve SKIPPED (--quick).")
|
||||
|
||||
# ---- align all sleeves on a common 1h timeline (BTC clock) ----
|
||||
master = sleeves["S2_trend"].index
|
||||
aligned = pd.DataFrame(index=master)
|
||||
for name, s in sleeves.items():
|
||||
aligned[name] = s.reindex(master).fillna(0.0)
|
||||
|
||||
# the portfolio is only meaningful where the slowest sleeve is live.
|
||||
# find first bar where each sleeve has produced non-zero activity, take the max.
|
||||
starts = {}
|
||||
for name in aligned.columns:
|
||||
nz = np.nonzero(aligned[name].to_numpy() != 0.0)[0]
|
||||
starts[name] = nz[0] if len(nz) else len(aligned)
|
||||
start = max(starts.values())
|
||||
aligned = aligned.iloc[start:]
|
||||
ts_a = pd.Series(aligned.index)
|
||||
print(f"\n Common active window: {aligned.index[0].date()} → {aligned.index[-1].date()} "
|
||||
f"({len(aligned)} bars). Sleeves: {list(aligned.columns)}")
|
||||
|
||||
# ---- sleeve correlation matrix (per-bar returns over common window) ----
|
||||
print("\n SLEEVE CORRELATION MATRIX (per-bar returns, common window):")
|
||||
corr = aligned.corr()
|
||||
cols = list(aligned.columns)
|
||||
print(" " + "".join(f"{c:>10}" for c in cols))
|
||||
for c in cols:
|
||||
print(f" {c:>9} " + "".join(f"{corr.loc[c, c2]:>+10.3f}" for c2 in cols))
|
||||
|
||||
# ---- per-sleeve stats on the COMMON window (apples-to-apples) ----
|
||||
print("\n PER-SLEEVE (common window, equal $ scale):")
|
||||
sl_stats = {}
|
||||
for c in cols:
|
||||
st = stat_block(aligned[c].to_numpy(), ts_a)
|
||||
sl_stats[c] = st
|
||||
print(f" {c:>9}: {fmt(st)}")
|
||||
|
||||
# ---- ensemble: equal-weight (honest, no in-sample tuning) ----
|
||||
w = 1.0 / len(cols)
|
||||
ens_eq_w = aligned.to_numpy() @ (np.ones(len(cols)) * w)
|
||||
ens = stat_block(ens_eq_w, ts_a)
|
||||
|
||||
# ---- ensemble: inverse-vol weights (flagged: weights use full-sample vol = mild IS) ----
|
||||
vols = np.array([np.std(aligned[c].to_numpy()) for c in cols])
|
||||
iv = (1.0 / np.where(vols == 0, np.nan, vols))
|
||||
iv = np.nan_to_num(iv); iv = iv / iv.sum()
|
||||
ens_iv = stat_block(aligned.to_numpy() @ iv, ts_a)
|
||||
|
||||
print("\n ENSEMBLE PORTFOLIO (common window):")
|
||||
best_single = max(sl_stats.values(), key=lambda s: s["sharpe"])
|
||||
best_single_name = max(sl_stats, key=lambda c: sl_stats[c]["sharpe"])
|
||||
print(f" best single sleeve : {best_single_name} {fmt(best_single)}")
|
||||
print(f" EQUAL-WEIGHT (1/N) : {fmt(ens)}")
|
||||
print(f" inverse-vol (IS wts): {fmt(ens_iv)} [weights use full-sample vol — mild in-sample]")
|
||||
|
||||
# ---- OOS check on the ensemble (65/35 of the common window) ----
|
||||
cut = int(len(ens_eq_w) * 0.65)
|
||||
ens_is = stat_block(ens_eq_w[:cut], ts_a.iloc[:cut])
|
||||
ens_oos = stat_block(ens_eq_w[cut:], ts_a.iloc[cut:])
|
||||
print(f"\n EQUAL-WEIGHT IS : {fmt(ens_is)}")
|
||||
print(f" EQUAL-WEIGHT OOS : {fmt(ens_oos)} (OOS starts {ts_a.iloc[cut].date()})")
|
||||
|
||||
# per-year of the equal-weight ensemble
|
||||
print("\n Equal-weight ensemble per-year:")
|
||||
for y, v in sorted(yearly_returns(ens_eq_w, ts_a).items()):
|
||||
print(f" {y}: {v*100:>+7.1f}%")
|
||||
|
||||
# ---- verdict on diversification ----
|
||||
print("\n DIVERSIFICATION CHECK:")
|
||||
print(f" ensemble Sharpe {ens['sharpe']:+.2f} vs best single {best_single['sharpe']:+.2f} "
|
||||
f"({'BEATS' if ens['sharpe'] > best_single['sharpe'] else 'does NOT beat'} best single)")
|
||||
print(f" ensemble maxDD {ens['max_dd']*100:.0f}% vs best single {best_single['max_dd']*100:.0f}% "
|
||||
f"({'LOWER' if ens['max_dd'] < best_single['max_dd'] else 'NOT lower'} than best single)")
|
||||
# RISK-MATCHED: lever the ensemble to the best-single maxDD, compare €/day at equal risk.
|
||||
# (Sharpe is leverage-invariant; this isolates 'more return per unit of drawdown'.)
|
||||
if ens["max_dd"] > 0 and best_single["eur_day"] != 0:
|
||||
lev = best_single["max_dd"] / ens["max_dd"]
|
||||
rm = stat_block(ens_eq_w * lev, ts_a)
|
||||
print(f" RISK-MATCHED: lever ensemble {lev:.2f}x to ~{best_single['max_dd']*100:.0f}% DD "
|
||||
f"→ €/d={rm['eur_day']:+.2f} (DD {rm['max_dd']*100:.0f}%) vs best-single €/d={best_single['eur_day']:+.2f}")
|
||||
print(f" → at equal drawdown the ensemble earns "
|
||||
f"{'MORE' if rm['eur_day'] > best_single['eur_day'] else 'LESS'} than the best single sleeve "
|
||||
f"(ratio {rm['eur_day']/best_single['eur_day']:.2f}); this tracks the Sharpe ratio.")
|
||||
if ens["eur_day"] > 0:
|
||||
print(f" ensemble €/day(2k) {ens['eur_day']:+.2f} vs target ~50.00 "
|
||||
f"→ ~{(50.0/ens['eur_day']):.0f}x short of the goal.")
|
||||
else:
|
||||
print(" ensemble €/day(2k) <= 0 → no earning engine.")
|
||||
|
||||
return ens, sl_stats, corr
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
def main():
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument("--quick", action="store_true", help="skip slow ML sleeve + smaller RV grid")
|
||||
ap.add_argument("--no-cache", action="store_true", help="recompute ML walk-forward proba")
|
||||
args = ap.parse_args()
|
||||
|
||||
t0 = time.time()
|
||||
rv_best, rv_data = part1_relative_value(quick=args.quick)
|
||||
part2_ensemble(rv_best, rv_data, quick=args.quick, no_cache=args.no_cache)
|
||||
print(f"\n(elapsed {time.time()-t0:.0f}s)")
|
||||
print("\n" + "=" * 104)
|
||||
print("See docs/diary/2026-06-19-trackE-xsec-ensemble.md for the full honest write-up.")
|
||||
print("=" * 104)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user