Initial implementation of BTC-ETH regime modeling project

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Simon Moisy 2025-10-10 14:57:51 +08:00
parent 5674806a57
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3.12

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{
// VS Code debug configurations
"version": "0.2.0",
"configurations": [
{
"name": "Run ETH/BTC Regime Modeling",
"type": "debugpy",
"request": "launch",
"program": "${workspaceFolder}/main.py",
"args": [
"--btc", "${workspaceFolder}/../data/btcusd_1-min_data.csv",
"--eth", "${workspaceFolder}/../data/ethusd_1min_ohlc.csv",
"--rules", "20min,21min,22min,23min,24min,25min,26min,27min,28min,29min,30min,31min,32min,33min,34min,35min,36min,37min,38min,39min,40min,41min,42min,43min,44min,45min,46min,47min,48min,49min,50min,51min,52min,53min,54min,55min,56min,57min,58min,59min,60min",
"--states", "3",
"--split", "2023-01-01",
"--horizon", "60"
],
"console": "integratedTerminal",
"cwd": "${workspaceFolder}",
"justMyCode": true,
"env": {
"PYTHONUNBUFFERED": "1"
}
}
]
}

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README.md
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# BTC_ETH_regime_predictor
# BTCETH Regime Modeling
This project builds and tests a **Hidden Markov Model (HMM)** that classifies structural market regimes in Bitcoin and Ethereum based on 1-minute OHLCV data from Bitstamp (https://www.kaggle.com/datasets/mczielinski/bitcoin-historical-data/ and https://www.kaggle.com/datasets/viniciusqroz/ethereum-historical-data).
It is designed to identify volatility and correlation phases—*risk-on*, *risk-off*, and *neutral*—and to evaluate how predictive or useful these regimes are across multiple timeframes and forecast horizons.
---
## Project Overview
### Goals
1. Detect repeating market “regimes” using unsupervised learning.
2. Evaluate how those regimes behave across timeframes and forecast horizons.
3. Use regime identification to **select trading strategies per market state**, rather than predict short-term direction.
### Datasets
Two synchronized 1-minute OHLCV datasets:
* `btcusd_1-min_data.csv`
* `ethusd_1min_ohlc.csv`
Both sourced from Bitstamp (Kaggle datasets).
---
## Architecture
### 1. `main.py`
Core experiment runner.
Implements:
* **Feature construction**:
* Multi-scale realized volatility (`rv_*`)
* Trend ratios (`trend_*`)
* Rolling BTCETH correlations (`corr_*`)
* Cross-asset beta and divergence
* Liquidity proxies (`volratio`, `vol_sum`, `vol_diff`)
* **Hidden Markov Model**: Gaussian emissions, diagonal covariance.
* **Randomized time-split validation**: multiple random train/test windows with configurable embargo gap to avoid leakage.
* **Metrics**:
* Hit rate (directional accuracy)
* Annualized Sharpe ratio of the regime-implied signal
* Mean ± std across random splits
This script explores model robustness across **different resample rules** (e.g. `30min`, `45min`, `1H`).
---
### 2. `main_conf_metrics.py`
Lightweight evaluator used for the **confidence and coverage sweep**.
* Adds a `--conf` parameter to control how confident the model must be before emitting a trade (pseudo-ETSC gate).
* Prints per-run metrics:
* `cov`: coverage (fraction of bars with predictions)
* `hit`: overall hit rate
* `hit_trades`: accuracy conditional on trading
* `Sharpe`: annualized risk-adjusted performance
Used by shell scripts to benchmark many timeframes and confidence thresholds.
---
### 3. Shell scripts
#### `run_grid.sh`
Runs a large grid of:
* multiple resample rules (e.g. 20 min 60 min),
* multiple horizons (e.g. 26 bars ahead).
#### `run_focus.sh`
Focuses on the most promising regions (3741 min, 4959 min)
and sweeps confidence thresholds (0.45 0.60).
Produces concise summary lines for each combination.
---
## Key Findings
1. **Optimal timeframe:**
~**35 45 minutes** consistently yields the highest Sharpe ratios (~2.22.3).
2. **Forecast horizon:**
Best performance around **two bars ahead** (~80 min look-ahead for 40 min bars).
3. **Confidence threshold:**
Little effect between 0.450.60; model already confident on > 90 % of bars.
4. **Interpretation:**
Regimes reflect volatility and structure, not raw direction.
Use them to switch *strategy archetypes* (trend vs. mean-reversion) rather than predict sign.
---
## Example Usage
### Single test
```bash
python main.py \
--btc ../data/btcusd_1-min_data.csv \
--eth ../data/ethusd_1min_ohlc.csv \
--rules "30min,45min,1H" \
--states 3 \
--horizon 60
```
### Confidence and coverage sweep
```bash
./run_focus.sh
```
---
## Typical Output
```
# Randomized time-split comparison
States=3 HorizonMin=60 Splits=8 TestBars=500 GapBars=24
rule splits hit sharpe
45min 8 0.4642 ± 0.0071 2.0575 ± 0.0413
39min 8 0.4662 ± 0.0083 2.3124 ± 0.0502
30min 8 0.4632 ± 0.0090 2.0331 ± 0.0368
```
---
## Interpretation for Strategy Design
| Regime Type | Market Traits | Suggested Strategy |
| -------------------- | ------------------------ | ------------------------- |
| High-vol / decoupled | large ETH/BTC divergence | Momentum / Breakout |
| Low-vol / correlated | calm, mean-reverting | Reversion / Market-Making |
| Neutral | noisy transitions | Flat / Reduced exposure |
---
## Requirements
* **Python ≥ 3.11**
* **Environment manager:** [**uv**](https://github.com/astral-sh/uv) (fast Python package installer and environment manager)
### Setup
Create and activate a local environment using **uv**:
```bash
# from the project root
uv venv
source .venv/bin/activate
# install dependencies
uv pip install numpy pandas scikit-learn hmmlearn
```
---
## Repository Structure
```
.
├── main.py # core HMM regime experiment with CV
├── main_conf_metrics.py # confidence/coverage sweep
├── run_grid.sh # full grid search over horizons/timeframes
├── run_focus.sh # focused confidence sweep
├── README.md
```

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#!/usr/bin/env python3
from __future__ import annotations
import argparse
from dataclasses import dataclass
from pathlib import Path
import numpy as np
import pandas as pd
from hmmlearn.hmm import GaussianHMM
from sklearn.preprocessing import StandardScaler
# ------------------------------- CLI -----------------------------------------
@dataclass
class CLI:
btc_csv: Path
eth_csv: Path
resample_rules: list[str]
n_states: int
horizon_min: int
# CV params
cv_splits: int
cv_test_bars: int
cv_gap_bars: int
cv_seed: int
cv_since: str | None # restrict sampling to recent era
def parse_args() -> CLI:
p = argparse.ArgumentParser(description="BTC/ETH regime modeling with randomized time splits")
p.add_argument("--btc", type=Path, default=Path("btcusd_1-min_data.csv"))
p.add_argument("--eth", type=Path, default=Path("ethusd_1min_ohlc.csv"))
p.add_argument("--rules", default="30min,45min,1H", help="Comma-separated pandas offsets")
p.add_argument("--states", type=int, default=3)
p.add_argument("--horizon", type=int, default=60)
# randomized CV controls
p.add_argument("--cv_splits", type=int, default=8, help="number of random test windows")
p.add_argument("--cv_test_bars", type=int, default=500, help="length of each test window in bars")
p.add_argument("--cv_gap_bars", type=int, default=24, help="embargo gap before test window")
p.add_argument("--cv_seed", type=int, default=7, help="rng seed for reproducibility")
p.add_argument("--cv_since", default=None, help="only sample test starts at/after this date (e.g. 2023-01-01)")
a = p.parse_args()
rules = [r.strip() for r in a.rules.split(",") if r.strip()]
return CLI(a.btc, a.eth, rules, a.states, a.horizon, a.cv_splits, a.cv_test_bars, a.cv_gap_bars, a.cv_seed, a.cv_since)
# ------------------------------ IO / CLEAN -----------------------------------
def _norm_headers(df: pd.DataFrame) -> pd.DataFrame:
df = df.rename(columns={c: c.strip().lower() for c in df.columns})
if "unix" in df.columns: df = df.rename(columns={"unix": "timestamp"})
if "date" in df.columns: df = df.rename(columns={"date": "timestamp"})
return df
def _load_bitstamp_csv(path: Path, prefix: str) -> pd.DataFrame:
df = pd.read_csv(path)
df = _norm_headers(df)
if "timestamp" not in df.columns: raise ValueError(f"Missing timestamp in {path}")
df["timestamp"] = pd.to_datetime(df["timestamp"], unit="s", utc=True, errors="coerce")
df = df.dropna(subset=["timestamp"]).set_index("timestamp").sort_index()
for c in ("open","high","low","close","volume"):
if c in df.columns: df[c] = pd.to_numeric(df[c], errors="coerce", downcast="float")
df = df[["open","high","low","close","volume"]].dropna()
return df.add_prefix(prefix + "_")
def _align_minutely(btc: pd.DataFrame, eth: pd.DataFrame) -> pd.DataFrame:
idx = btc.index.intersection(eth.index)
df = btc.reindex(idx).join(eth.reindex(idx), how="inner")
return df.ffill(limit=60).dropna()
# --------------------------- FEATURES (same as before) -----------------------
def build_features(df: pd.DataFrame, rule: str, horizon_min: int) -> pd.DataFrame:
df = df.copy()
df["btc_ret"] = np.log(df["btc_close"]).diff()
df["eth_ret"] = np.log(df["eth_close"]).diff()
df["ratio"] = df["eth_close"]/df["btc_close"]
df["ratio_ret"] = np.log(df["ratio"]).diff()
for win in (15,30,60,120,240,360):
df[f"rv_{win}m"] = df["ratio_ret"].rolling(win, min_periods=win).std()
for win in (60,240,1440):
ma = df["ratio"].rolling(win, min_periods=win).mean()
df[f"trend_{win}m"] = df["ratio"]/(ma+1e-12)-1.0
for win in (60,120,240):
df[f"corr_{win}m"] = df["btc_ret"].rolling(win, min_periods=win).corr(df["eth_ret"])
cov_120 = df["eth_ret"].rolling(120, min_periods=120).cov(df["btc_ret"])
var_120 = df["btc_ret"].rolling(120, min_periods=120).var()
df["beta_2h"] = cov_120/(var_120+1e-12)
std_b = df["btc_ret"].rolling(120, min_periods=120).std()
std_e = df["eth_ret"].rolling(120, min_periods=120).std()
df["divergence_2h"] = np.abs(df["btc_ret"]/(std_b+1e-12) - df["eth_ret"]/(std_e+1e-12))
df["volratio"] = np.log((df["eth_volume"]+1e-9)/(df["btc_volume"]+1e-9))
df["vol_sum"] = np.log(df["eth_volume"]+df["btc_volume"]+1e-9)
df["vol_diff"] = (df["eth_volume"]-df["btc_volume"])/(df["eth_volume"]+df["btc_volume"]+1e-9)
df["rv_2h"] = df.get("rv_120m", df["ratio_ret"].rolling(120, min_periods=120).std())
df["corr_2h"] = df.get("corr_120m", df["btc_ret"].rolling(120, min_periods=120).corr(df["eth_ret"]))
df["ratio_trend"] = df.get("trend_1440m", df["ratio"]/(df["ratio"].rolling(1440, min_periods=1440).mean()+1e-12)-1.0)
agg = {"btc_close":"last","eth_close":"last","ratio":"last","ratio_ret":"sum"}
for c in df.columns:
if c not in agg: agg[c] = "mean"
g = df.resample(rule).agg(agg).dropna()
step_min = max(1, int(pd.Timedelta(rule).total_seconds()//60))
ahead = max(1, int(round(horizon_min/step_min)))
g["fut_ret"] = g["ratio_ret"].shift(-ahead)
return g.dropna()
def feature_matrix(g: pd.DataFrame) -> tuple[np.ndarray,np.ndarray,list[str]]:
ban = {"fut_ret","btc_close","eth_close","ratio"}
keep = ("rv_","corr_","trend_","beta_","divergence_","vol")
feats = []
if "ratio_ret" in g.columns:
feats.append("ratio_ret")
feats += [c for c in g.columns if c not in ban and c!="ratio_ret" and any(c.startswith(p) for p in keep)]
if not feats: feats = ["ratio_ret","rv_30m","rv_2h","corr_2h","ratio_trend","volratio"]
X = g[feats].astype(np.float32).values
y = g["fut_ret"].astype(np.float32).values
return X,y,feats
# ------------------------- Randomized time splits -----------------------------
def sample_random_splits(
g: pd.DataFrame,
n_splits: int,
test_bars: int,
gap_bars: int,
seed: int,
since: str | None
):
rng = np.random.default_rng(seed)
idx = g.index
if since is not None:
idx = idx[idx >= pd.Timestamp(since, tz="UTC")]
# valid start indices ensure full test window fits
valid = np.arange(len(idx) - test_bars)
if len(valid) <= 0:
raise ValueError("Not enough data for requested test window")
starts = rng.choice(valid, size=min(n_splits, len(valid)), replace=False)
for s in np.sort(starts):
test_start = idx[s]
test_end = idx[s + test_bars - 1]
# train uses all data strictly before (test_start - gap)
embargo_end = idx[max(0, s - gap_bars - 1)] if s - gap_bars - 1 >= 0 else None
train = g.loc[:embargo_end] if embargo_end is not None else g.iloc[0:0]
test = g.loc[test_start:test_end]
if len(train) == 0 or len(test) < test_bars: # skip degenerate
continue
yield train, test, (test_start, test_end)
# ------------------------------ Model / Fit -----------------------------------
def fit_and_predict_train_test(train: pd.DataFrame, test: pd.DataFrame, n_states: int):
Xtr, ytr, feats = feature_matrix(train)
Xte, yte, _ = feature_matrix(test)
scaler = StandardScaler()
Xtr_s = scaler.fit_transform(Xtr)
Xte_s = scaler.transform(Xte)
hmm = GaussianHMM(n_components=n_states, covariance_type="diag", n_iter=300, random_state=7)
hmm.fit(Xtr_s)
st_tr = hmm.predict(Xtr_s)
st_te = hmm.predict(Xte_s)
means = {s: float(np.nanmean(ytr[st_tr == s])) for s in range(n_states)}
small = np.nanpercentile(np.abs(list(means.values())), 30)
state_to_stance = {s: (1 if m > +small else (-1 if m < -small else 0)) for s, m in means.items()}
preds = np.vectorize(state_to_stance.get)(st_te).astype(np.int8)
return preds, yte, state_to_stance, feats
def metrics(y: np.ndarray, preds: np.ndarray, rule: str) -> dict[str,float]:
T = min(len(y), len(preds)); y, preds = y[:T], preds[:T]
pnl = preds * y
hit = (np.sign(preds) == np.sign(y)).mean() if T else np.nan
bars_per_day = int(round(24 * 60 / max(1, int(pd.Timedelta(rule).total_seconds() // 60))))
ann = np.sqrt(365 * bars_per_day)
sharpe = float(np.nanmean(pnl) / (np.nanstd(pnl) + 1e-12) * ann)
return {"hit_rate": float(hit), "ann_sharpe": sharpe, "n_points": int(T)}
# ------------------------------ Runner ---------------------------------------
def run_rule_mc(minute: pd.DataFrame, rule: str, n_states: int, horizon_min: int, cv) -> dict:
g = build_features(minute, rule, horizon_min)
rows = []
for train, test, (ts, te) in sample_random_splits(g, cv.cv_splits, cv.cv_test_bars, cv.cv_gap_bars, cv.cv_seed, cv.cv_since):
preds, ytest, state_map, feats = fit_and_predict_train_test(train, test, n_states)
m = metrics(ytest, preds, rule)
rows.append({"hit_rate": m["hit_rate"], "ann_sharpe": m["ann_sharpe"], "n_points": m["n_points"], "test_span": (ts, te)})
if not rows:
return {"rule": rule, "hit_mean": np.nan, "sharpe_mean": np.nan, "splits": 0, "hit_std": np.nan, "sharpe_std": np.nan}
hits = np.array([r["hit_rate"] for r in rows], dtype=float)
sharpes = np.array([r["ann_sharpe"] for r in rows], dtype=float)
return {
"rule": rule,
"hit_mean": float(np.nanmean(hits)),
"hit_std": float(np.nanstd(hits)),
"sharpe_mean": float(np.nanmean(sharpes)),
"sharpe_std": float(np.nanstd(sharpes)),
"splits": len(rows),
}
# ------------------------------ MAIN -----------------------------------------
def main(args: CLI) -> None:
btc = _load_bitstamp_csv(args.btc_csv, "btc")
eth = _load_bitstamp_csv(args.eth_csv, "eth")
minute = _align_minutely(btc, eth)
class CV: pass
cv = CV(); cv.cv_splits=args.cv_splits; cv.cv_test_bars=args.cv_test_bars; cv.cv_gap_bars=args.cv_gap_bars
cv.cv_seed=args.cv_seed; cv.cv_since=args.cv_since
results = [run_rule_mc(minute, rule, args.n_states, args.horizon_min, cv) for rule in args.resample_rules]
df = pd.DataFrame(results).sort_values(by="sharpe_mean", ascending=False)
print("# Randomized time-split comparison")
print(f"States={args.n_states} HorizonMin={args.horizon_min} Splits={args.cv_splits} TestBars={args.cv_test_bars} GapBars={args.cv_gap_bars} Since={args.cv_since}")
if not df.empty:
df["hit"] = df["hit_mean"].round(4).astype(str) + " ± " + df["hit_std"].round(4).astype(str)
df["sharpe"]= df["sharpe_mean"].round(4).astype(str) + " ± " + df["sharpe_std"].round(4).astype(str)
print(df[["rule","splits","hit","sharpe"]].to_string(index=False))
else:
print("No valid splits found")
if __name__ == "__main__":
args = parse_args()
main(args)

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#!/usr/bin/env python3
import argparse, numpy as np, pandas as pd
from pathlib import Path
from hmmlearn.hmm import GaussianHMM
from sklearn.preprocessing import StandardScaler
from main import _load_bitstamp_csv, _align_minutely, build_features, feature_matrix # reuse helpers
p = argparse.ArgumentParser()
p.add_argument("--btc", type=Path)
p.add_argument("--eth", type=Path)
p.add_argument("--rules")
p.add_argument("--states", type=int)
p.add_argument("--split")
p.add_argument("--horizon", type=int)
p.add_argument("--conf", type=float)
a = p.parse_args()
btc = _load_bitstamp_csv(a.btc, "btc")
eth = _load_bitstamp_csv(a.eth, "eth")
minute = _align_minutely(btc, eth)
g = build_features(minute, a.rules, a.horizon)
train, test = g.loc[:a.split], g.loc[a.split:]
Xtr, ytr, _ = feature_matrix(train)
Xte, yte, _ = feature_matrix(test)
scaler = StandardScaler()
Xtr_s, Xte_s = scaler.fit_transform(Xtr), scaler.transform(Xte)
hmm = GaussianHMM(n_components=a.states, covariance_type="diag", n_iter=300, random_state=7)
hmm.fit(Xtr_s)
st_tr, st_te = hmm.predict(Xtr_s), hmm.predict(Xte_s)
means = {s: float(np.nanmean(ytr[st_tr == s])) for s in range(a.states)}
thr = np.nanpercentile(np.abs(list(means.values())), 30)
state_to_stance = {s: (1 if m > +thr else (-1 if m < -thr else 0)) for s, m in means.items()}
post_te = hmm.predict_proba(Xte_s)
maxp = post_te.max(axis=1)
raw_pred = np.vectorize(state_to_stance.get)(st_te)
preds = np.where(maxp >= a.conf, raw_pred, 0).astype(np.int8)
# Metrics
y, preds = yte[:len(preds)], preds
mask = preds != 0
coverage = mask.mean()
hit_rate = (np.sign(preds) == np.sign(y)).mean()
hit_trades = (np.sign(preds[mask]) == np.sign(y[mask])).mean() if mask.any() else np.nan
pnl = preds * y
bars_day = int(round(24*60 / max(1,int(pd.Timedelta(a.rules).total_seconds()/60))))
ann = np.sqrt(365 * bars_day)
sharpe = np.nanmean(pnl) / (np.nanstd(pnl)+1e-12) * ann
print(f"{a.rules:>5} conf={a.conf:.2f} cov={coverage:.3f} hit={hit_rate:.3f} "
f"hit_trades={hit_trades:.3f} Sharpe={sharpe:.3f}")

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[project]
name = "btc-eth-regime-predictor"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.12"
dependencies = [
"hmmlearn>=0.3.3",
"matplotlib>=3.10.6",
"numpy>=2.3.3",
"pandas>=2.3.3",
]

46
run_focus.sh Executable file
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@ -0,0 +1,46 @@
#!/usr/bin/env bash
# run_focus.sh — focused grid with coverage and confidence sweep
set -euo pipefail
PROJ_DIR="${PROJ_DIR:-$HOME/Documents/Work/TCP/BTC_ETH_regime_predictor}"
DATA_DIR="${DATA_DIR:-$PROJ_DIR/../data}"
PY="${PY:-$PROJ_DIR/.venv/bin/python}"
BTC="$DATA_DIR/btcusd_1-min_data.csv"
ETH="$DATA_DIR/ethusd_1min_ohlc.csv"
SPLIT="2023-01-01"
N_STATES=3
BARS_AHEAD=2 # fixed
CONF_LIST=(0.45 0.50 0.55 0.60)
# Rule bands 3741min and 4959min
readarray -t RULES < <(seq 37 41 | awk '{printf "%dmin\n",$1}')
readarray -t RULES2 < <(seq 49 59 | awk '{printf "%dmin\n",$1}')
RULES=("${RULES[@]}" "${RULES2[@]}")
to_minutes() { echo "${1%min}"; }
OUT_DIR="$PROJ_DIR/run_logs"
mkdir -p "$OUT_DIR"
LOG="$OUT_DIR/focus_$(date +%Y%m%d_%H%M%S).log"
for CONF in "${CONF_LIST[@]}"; do
echo "### Confidence=$CONF" | tee -a "$LOG"
for RULE in "${RULES[@]}"; do
BAR_MIN=$(to_minutes "$RULE")
HORIZON=$((BARS_AHEAD * BAR_MIN))
echo "Running rule='$RULE' horizon_min=$HORIZON conf=$CONF" | tee -a "$LOG"
"$PY" "$PROJ_DIR/main_conf_metrics.py" \
--btc "$BTC" \
--eth "$ETH" \
--rules "$RULE" \
--states "$N_STATES" \
--split "$SPLIT" \
--horizon "$HORIZON" \
--conf "$CONF" | tee -a "$LOG"
echo | tee -a "$LOG"
done
done

55
run_grid.sh Executable file
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@ -0,0 +1,55 @@
#!/usr/bin/env bash
# run_grid.sh — loop over timeframes and 26 bars-ahead horizons
set -euo pipefail
# Paths (edit if different)
PROJ_DIR="${PROJ_DIR:-$HOME/Documents/Work/TCP/BTC_ETH_regime_predictor}"
DATA_DIR="${DATA_DIR:-$PROJ_DIR/../data}"
PY="${PY:-$PROJ_DIR/.venv/bin/python}"
BTC_CSV="${BTC_CSV:-$DATA_DIR/btcusd_1-min_data.csv}"
ETH_CSV="${ETH_CSV:-$DATA_DIR/ethusd_1min_ohlc.csv}"
SPLIT_DATE="${SPLIT_DATE:-2023-01-01}"
N_STATES="${N_STATES:-3}"
# Timeframes to test: 2060 min inclusive
readarray -t RULES < <(seq 20 60 | awk '{printf "%dmin\n",$1}')
# Convert a pandas-like offset to minutes: supports Nmin, NH, ND
to_minutes() {
local r="$1"
if [[ "$r" =~ ^([0-9]+)min$ ]]; then
echo "${BASH_REMATCH[1]}"
elif [[ "$r" =~ ^([0-9]+)H$ ]]; then
echo $(( ${BASH_REMATCH[1]} * 60 ))
elif [[ "$r" =~ ^([0-9]+)D$ ]]; then
echo $(( ${BASH_REMATCH[1]} * 1440 ))
else
echo "Unsupported rule: $r" >&2
exit 2
fi
}
# Logs
OUT_DIR="${OUT_DIR:-$PROJ_DIR/run_logs}"
mkdir -p "$OUT_DIR"
TS="$(date +"%Y%m%d_%H%M%S")"
LOG="$OUT_DIR/grid_${TS}.log"
# Run grid
for BARS in 2 3 4 5 6; do
for RULE in "${RULES[@]}"; do
BAR_MIN=$(to_minutes "$RULE")
HORIZON=$(( BARS * BAR_MIN ))
echo "Running rule='$RULE' bars_ahead=$BARS horizon_min=$HORIZON" | tee -a "$LOG"
"$PY" "$PROJ_DIR/main.py" \
--btc "$BTC_CSV" \
--eth "$ETH_CSV" \
--rules "$RULE" \
--states "$N_STATES" \
--split "$SPLIT_DATE" \
--horizon "$HORIZON" | tee -a "$LOG"
echo | tee -a "$LOG"
done
done

633
uv.lock generated Normal file
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version = 1
revision = 3
requires-python = ">=3.12"
[[package]]
name = "btc-eth-regime-predictor"
version = "0.1.0"
source = { virtual = "." }
dependencies = [
{ name = "hmmlearn" },
{ name = "matplotlib" },
{ name = "numpy" },
{ name = "pandas" },
]
[package.metadata]
requires-dist = [
{ name = "hmmlearn", specifier = ">=0.3.3" },
{ name = "matplotlib", specifier = ">=3.10.6" },
{ name = "numpy", specifier = ">=2.3.3" },
{ name = "pandas", specifier = ">=2.3.3" },
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