Codex
590 lines
22 KiB
Python
590 lines
22 KiB
Python
from __future__ import annotations
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import argparse
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import json
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import math
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import sys
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from dataclasses import dataclass
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Any
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PROJECT_ROOT = Path(__file__).resolve().parents[1]
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if str(PROJECT_ROOT) not in sys.path:
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sys.path.insert(0, str(PROJECT_ROOT))
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try:
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import torch
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from torch import nn
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from torch.utils.data import DataLoader, TensorDataset
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except ImportError as exc: # pragma: no cover - exercised on machines without training deps.
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raise SystemExit(
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"PyTorch is not installed. Install local training deps with: "
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"python -m pip install torch --index-url https://download.pytorch.org/whl/cpu"
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) from exc
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from crypto_spot_bot.bybit import BybitClient
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from crypto_spot_bot.config import load_settings
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from crypto_spot_bot.indicators import add_indicators
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from crypto_spot_bot.models import Candle
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from crypto_spot_bot.time_series import DEFAULT_TORCH_FEATURES, _feature_matrix, _log_returns
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@dataclass(slots=True)
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class PreparedData:
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train_x: torch.Tensor
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train_y: torch.Tensor
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validation_x: torch.Tensor
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validation_y: torch.Tensor
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validation_targets: list[float]
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feature_names: list[str]
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feature_means: list[float]
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feature_scales: list[float]
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target_mean: float
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target_scale: float
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train_samples: int
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validation_samples: int
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class RecurrentReturnModel(nn.Module):
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def __init__(
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self,
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*,
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architecture: str,
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input_size: int,
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hidden_size: int,
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num_layers: int,
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dropout: float,
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) -> None:
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super().__init__()
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recurrent_cls = nn.LSTM if architecture == "lstm" else nn.GRU
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self.rnn = recurrent_cls(
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input_size=input_size,
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hidden_size=hidden_size,
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num_layers=num_layers,
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dropout=dropout if num_layers > 1 else 0.0,
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batch_first=True,
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)
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self.head = nn.Linear(hidden_size, 1)
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def forward(self, values: torch.Tensor) -> torch.Tensor:
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output, _state = self.rnn(values)
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return self.head(output[:, -1, :]).squeeze(-1)
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def main() -> None:
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args = _parse_args()
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if args.threads > 0:
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torch.set_num_threads(args.threads)
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_seed(args.seed)
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settings = load_settings(args.env)
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client = BybitClient(settings)
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symbols = _symbols(args.symbols, settings, client)
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interval = args.interval or settings.base_interval
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output = Path(args.output) if args.output else settings.time_series_lstm_model_path
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device = _device(args.device)
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horizon = args.horizon if args.horizon > 0 else max(1, settings.time_series_forecast_horizon)
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feature_names = _feature_names_arg(args.features)
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artifact: dict[str, Any] = {
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"version": 3,
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"type": "pytorch_recurrent_forecaster",
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"created_at": datetime.now(timezone.utc).isoformat(),
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"trainer": Path(__file__).name,
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"interval": interval,
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"limit": args.limit,
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"validation_window": args.validation_window,
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"target_horizon": horizon,
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"direct_horizon": True,
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"feature_names": feature_names,
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"feature_count": len(feature_names),
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"device": str(device),
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"symbols": {},
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}
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for symbol in symbols:
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result = _train_symbol(
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client=client,
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symbol=symbol,
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interval=interval,
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limit=args.limit,
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validation_window=args.validation_window,
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target_horizon=horizon,
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feature_names=feature_names,
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architectures=_strings(args.architectures),
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lookbacks=_ints(args.lookbacks),
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hidden_sizes=_ints(args.hidden_sizes),
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layers_values=_ints(args.layers),
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dropouts=_floats(args.dropouts),
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epochs=args.epochs,
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patience=args.patience,
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batch_size=args.batch_size,
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learning_rate=args.learning_rate,
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weight_decay=args.weight_decay,
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clip=args.clip,
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device=device,
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seed=args.seed,
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)
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if result is None:
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print(f"{symbol}: skipped, not enough candles or train/validation samples")
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continue
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artifact["symbols"][symbol] = result
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print(
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f"{symbol}: model={result['model']} lookback={result['lookback']} "
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f"features={result['input_size']} hidden={result['hidden_size']} "
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f"layers={result['num_layers']} horizon={result['target_horizon']} "
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f"mae={result['validation_mae_percent']:.5f}% "
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f"baseline={result['baseline_mae_percent']:.5f}% "
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f"skill={result['skill']:.4f} dir={result['directional_accuracy']:.3f}"
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)
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output.parent.mkdir(parents=True, exist_ok=True)
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tmp_output = output.with_name(f"{output.name}.tmp")
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tmp_output.write_text(json.dumps(artifact, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
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tmp_output.replace(output)
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print(f"saved {output}")
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def _parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description="Train PyTorch LSTM/GRU forecast models on Bybit spot candles.")
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parser.add_argument("--env", default=None, help="Path to .env file.")
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parser.add_argument("--symbols", default="", help="Comma-separated symbols. Defaults to configured or popular pairs.")
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parser.add_argument("--interval", default="", help="Bybit kline interval. Defaults to BASE_INTERVAL.")
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parser.add_argument("--limit", type=int, default=1000, help="Kline limit per symbol.")
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parser.add_argument("--validation-window", type=int, default=120, help="Held-out tail targets used for validation.")
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parser.add_argument("--horizon", type=int, default=0, help="Direct forecast horizon in candles. Defaults to TIME_SERIES_FORECAST_HORIZON.")
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parser.add_argument("--features", default=",".join(DEFAULT_TORCH_FEATURES), help="Comma-separated feature names.")
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parser.add_argument("--architectures", default="lstm,gru", help="Comma-separated recurrent types: lstm,gru.")
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parser.add_argument("--lookbacks", default="32,64", help="Comma-separated sequence lengths.")
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parser.add_argument("--hidden-sizes", default="32,64", help="Comma-separated hidden sizes.")
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parser.add_argument("--layers", default="2", help="Comma-separated recurrent layer counts.")
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parser.add_argument("--dropouts", default="0.15", help="Comma-separated dropout values; only used with layers > 1.")
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parser.add_argument("--epochs", type=int, default=60, help="Maximum epochs per hyperparameter candidate.")
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parser.add_argument("--patience", type=int, default=10, help="Early stopping patience in epochs.")
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parser.add_argument("--batch-size", type=int, default=64, help="Training batch size.")
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parser.add_argument("--learning-rate", type=float, default=0.001, help="AdamW learning rate.")
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parser.add_argument("--weight-decay", type=float, default=0.0001, help="AdamW weight decay.")
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parser.add_argument("--clip", type=float, default=8.0, help="Clamp normalized features, targets and predictions.")
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parser.add_argument("--seed", type=int, default=7, help="Random seed.")
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parser.add_argument("--threads", type=int, default=0, help="Torch CPU threads; 0 keeps torch default.")
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parser.add_argument("--device", default="auto", help="auto, cpu, cuda, or mps.")
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parser.add_argument("--output", default="", help="Output JSON path. Defaults to TIME_SERIES_LSTM_MODEL_PATH.")
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return parser.parse_args()
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def _symbols(raw: str, settings: Any, client: BybitClient) -> list[str]:
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if raw.strip():
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return [item.strip().upper() for item in raw.split(",") if item.strip()]
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if settings.symbols:
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return list(settings.symbols)
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return client.popular_spot_symbols(settings.top_symbols_count)
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def _train_symbol(
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*,
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client: BybitClient,
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symbol: str,
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interval: str,
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limit: int,
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validation_window: int,
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target_horizon: int,
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feature_names: list[str],
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architectures: list[str],
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lookbacks: list[int],
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hidden_sizes: list[int],
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layers_values: list[int],
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dropouts: list[float],
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epochs: int,
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patience: int,
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batch_size: int,
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learning_rate: float,
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weight_decay: float,
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clip: float,
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device: torch.device,
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seed: int,
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) -> dict[str, Any] | None:
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candles = client.klines(symbol, interval, limit)
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add_indicators(candles)
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closes = [float(candle.close) for candle in candles if candle.close > 0]
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returns = _log_returns(closes)
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if len(candles) < max(140, validation_window + max(lookbacks) + target_horizon + 16):
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return None
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best: dict[str, Any] | None = None
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for lookback in lookbacks:
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prepared = _prepare_data(
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candles=candles,
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feature_names=feature_names,
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lookback=lookback,
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target_horizon=target_horizon,
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validation_window=validation_window,
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clip=clip,
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device=device,
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)
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if prepared is None:
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continue
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baseline_mae = sum(abs(value) for value in prepared.validation_targets) / len(prepared.validation_targets)
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for architecture in architectures:
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if architecture not in {"lstm", "gru"}:
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continue
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for hidden_size in hidden_sizes:
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for num_layers in layers_values:
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for dropout in dropouts:
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if num_layers <= 1 and dropout != 0.0:
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continue
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candidate = _fit_candidate(
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prepared=prepared,
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architecture=architecture,
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input_size=len(feature_names),
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hidden_size=hidden_size,
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num_layers=num_layers,
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dropout=dropout,
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epochs=epochs,
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patience=patience,
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batch_size=batch_size,
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learning_rate=learning_rate,
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weight_decay=weight_decay,
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clip=clip,
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device=device,
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seed=seed,
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)
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validation_mae = float(candidate["validation_mae"])
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skill = (baseline_mae - validation_mae) / baseline_mae if baseline_mae > 0 else 0.0
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row = {
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**candidate,
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"model": f"torch_{architecture}",
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"architecture": architecture,
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"lookback": lookback,
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"target_horizon": target_horizon,
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"direct_horizon": True,
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"input_size": len(feature_names),
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"feature_names": feature_names,
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"feature_means": prepared.feature_means,
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"feature_scales": prepared.feature_scales,
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"target_mean": prepared.target_mean,
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"target_scale": prepared.target_scale,
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"mean": prepared.target_mean,
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"scale": prepared.target_scale,
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"hidden_size": hidden_size,
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"num_layers": num_layers,
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"dropout": dropout if num_layers > 1 else 0.0,
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"clip": clip,
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"validation_mae_percent": validation_mae * 100,
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"baseline_mae_percent": baseline_mae * 100,
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"skill": skill,
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"candles": len(candles),
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"returns": len(returns),
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"train_samples": prepared.train_samples,
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"validation_samples": prepared.validation_samples,
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}
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score = _candidate_score(row)
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if best is None or score < _candidate_score(best):
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best = row
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if best is None:
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return None
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best.pop("validation_mae", None)
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return best
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def _prepare_data(
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*,
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candles: list[Candle],
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feature_names: list[str],
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lookback: int,
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target_horizon: int,
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validation_window: int,
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clip: float,
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device: torch.device,
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) -> PreparedData | None:
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closes = [float(candle.close) for candle in candles]
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feature_rows = _feature_matrix(candles, feature_names)
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samples: list[tuple[list[list[float]], float]] = []
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for end_index in range(lookback - 1, len(candles) - target_horizon):
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current = closes[end_index]
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future = closes[end_index + target_horizon]
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if current <= 0 or future <= 0:
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continue
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window = feature_rows[end_index - lookback + 1 : end_index + 1]
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if len(window) != lookback:
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continue
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samples.append((window, math.log(future / current)))
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if len(samples) < 48:
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return None
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validation_window = min(max(16, validation_window), max(16, len(samples) // 3))
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train_samples = samples[:-validation_window]
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validation_samples = samples[-validation_window:]
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if len(train_samples) < 24 or len(validation_samples) < 8:
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return None
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feature_means, feature_scales = _feature_stats(train_samples, len(feature_names))
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train_targets = [target for _, target in train_samples]
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target_mean = sum(train_targets) / len(train_targets)
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target_scale = _return_scale(train_targets)
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train_x, train_y = _normalize_samples(
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train_samples,
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feature_means=feature_means,
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feature_scales=feature_scales,
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target_mean=target_mean,
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target_scale=target_scale,
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clip=clip,
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)
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validation_x, validation_y = _normalize_samples(
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validation_samples,
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feature_means=feature_means,
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feature_scales=feature_scales,
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target_mean=target_mean,
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target_scale=target_scale,
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clip=clip,
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)
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return PreparedData(
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train_x=torch.tensor(train_x, dtype=torch.float32, device=device),
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train_y=torch.tensor(train_y, dtype=torch.float32, device=device),
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validation_x=torch.tensor(validation_x, dtype=torch.float32, device=device),
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validation_y=torch.tensor(validation_y, dtype=torch.float32, device=device),
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validation_targets=[target for _, target in validation_samples],
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feature_names=feature_names,
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feature_means=feature_means,
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feature_scales=feature_scales,
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target_mean=target_mean,
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target_scale=target_scale,
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train_samples=len(train_x),
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validation_samples=len(validation_x),
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)
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def _feature_stats(samples: list[tuple[list[list[float]], float]], input_size: int) -> tuple[list[float], list[float]]:
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columns = [[] for _ in range(input_size)]
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for window, _target in samples:
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for row in window:
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for index in range(input_size):
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columns[index].append(float(row[index] if index < len(row) else 0.0))
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means: list[float] = []
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scales: list[float] = []
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for values in columns:
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if not values:
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means.append(0.0)
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scales.append(1.0)
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continue
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mean = sum(values) / len(values)
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deviations = sorted(abs(value - mean) for value in values)
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mad = deviations[len(deviations) // 2] if deviations else 0.0
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mean_abs = sum(deviations) / len(deviations) if deviations else 0.0
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means.append(mean)
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scales.append(max(mad, mean_abs * 0.5, 1e-6))
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return means, scales
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def _normalize_samples(
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samples: list[tuple[list[list[float]], float]],
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*,
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feature_means: list[float],
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feature_scales: list[float],
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target_mean: float,
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target_scale: float,
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clip: float,
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) -> tuple[list[list[list[float]]], list[float]]:
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input_size = len(feature_means)
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x_values: list[list[list[float]]] = []
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y_values: list[float] = []
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for window, target in samples:
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x_values.append(
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[
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[
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_clamp(
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((row[index] if index < len(row) else 0.0) - feature_means[index])
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/ max(feature_scales[index], 1e-8),
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-clip,
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clip,
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)
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for index in range(input_size)
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]
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for row in window
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]
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)
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y_values.append(_clamp((target - target_mean) / max(target_scale, 1e-8), -clip, clip))
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return x_values, y_values
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def _fit_candidate(
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*,
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prepared: PreparedData,
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architecture: str,
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input_size: int,
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hidden_size: int,
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num_layers: int,
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dropout: float,
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epochs: int,
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patience: int,
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batch_size: int,
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learning_rate: float,
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weight_decay: float,
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clip: float,
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device: torch.device,
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seed: int,
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) -> dict[str, Any]:
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_seed(seed)
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model = RecurrentReturnModel(
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architecture=architecture,
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input_size=input_size,
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hidden_size=hidden_size,
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num_layers=num_layers,
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dropout=dropout,
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).to(device)
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optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
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criterion = nn.SmoothL1Loss(beta=0.5)
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generator = torch.Generator(device="cpu").manual_seed(seed)
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loader = DataLoader(
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TensorDataset(prepared.train_x, prepared.train_y),
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batch_size=max(1, batch_size),
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shuffle=True,
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generator=generator,
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)
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best_state: dict[str, torch.Tensor] | None = None
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best_metrics: dict[str, float] = {"validation_mae": math.inf, "directional_accuracy": 0.0, "buy_precision": 0.0}
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best_epoch = 0
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stale_epochs = 0
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for epoch in range(1, max(1, epochs) + 1):
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model.train()
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for batch_x, batch_y in loader:
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optimizer.zero_grad(set_to_none=True)
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loss = criterion(model(batch_x), batch_y)
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loss.backward()
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nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
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optimizer.step()
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metrics = _validation_metrics(model, prepared, clip)
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if metrics["validation_mae"] + 1e-12 < best_metrics["validation_mae"]:
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best_metrics = metrics
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best_epoch = epoch
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best_state = {key: value.detach().cpu().clone() for key, value in model.state_dict().items()}
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stale_epochs = 0
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else:
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stale_epochs += 1
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|
if stale_epochs >= max(1, patience):
|
|
break
|
|
|
|
if best_state:
|
|
model.load_state_dict(best_state)
|
|
return {
|
|
**best_metrics,
|
|
"best_epoch": best_epoch,
|
|
"epochs_trained": best_epoch + stale_epochs,
|
|
"state_dict": _export_recurrent_state(model),
|
|
"head_weight": _round_list(model.head.weight.detach().cpu().squeeze(0).tolist()),
|
|
"head_bias": round(float(model.head.bias.detach().cpu().item()), 10),
|
|
}
|
|
|
|
|
|
def _validation_metrics(model: nn.Module, prepared: PreparedData, clip: float) -> dict[str, float]:
|
|
model.eval()
|
|
with torch.no_grad():
|
|
normalized_predictions = model(prepared.validation_x).detach().cpu().tolist()
|
|
predictions = [
|
|
_clamp(float(prediction), -clip, clip) * prepared.target_scale + prepared.target_mean
|
|
for prediction in normalized_predictions
|
|
]
|
|
errors = [abs(prediction - actual) for prediction, actual in zip(predictions, prepared.validation_targets)]
|
|
correct = [
|
|
1.0
|
|
for prediction, actual in zip(predictions, prepared.validation_targets)
|
|
if (prediction > 0 and actual > 0) or (prediction < 0 and actual < 0)
|
|
]
|
|
non_zero = [
|
|
1.0
|
|
for prediction, actual in zip(predictions, prepared.validation_targets)
|
|
if prediction != 0 and actual != 0
|
|
]
|
|
buy_predictions = [
|
|
actual
|
|
for prediction, actual in zip(predictions, prepared.validation_targets)
|
|
if prediction > 0
|
|
]
|
|
buy_wins = [actual for actual in buy_predictions if actual > 0]
|
|
return {
|
|
"validation_mae": sum(errors) / len(errors) if errors else math.inf,
|
|
"directional_accuracy": len(correct) / len(non_zero) if non_zero else 0.0,
|
|
"buy_precision": len(buy_wins) / len(buy_predictions) if buy_predictions else 0.0,
|
|
}
|
|
|
|
|
|
def _candidate_score(row: dict[str, Any]) -> float:
|
|
mae = float(row["validation_mae"])
|
|
skill = float(row.get("skill", 0.0))
|
|
directional = float(row.get("directional_accuracy", 0.0))
|
|
buy_precision = float(row.get("buy_precision", 0.0))
|
|
return mae * (1.0 - max(0.0, skill) * 0.05) * (1.0 - max(0.0, directional - 0.5) * 0.03) * (
|
|
1.0 - max(0.0, buy_precision - 0.5) * 0.02
|
|
)
|
|
|
|
|
|
def _export_recurrent_state(model: RecurrentReturnModel) -> dict[str, Any]:
|
|
return {
|
|
key: _round_nested(value.detach().cpu().tolist())
|
|
for key, value in model.rnn.state_dict().items()
|
|
}
|
|
|
|
|
|
def _device(raw: str) -> torch.device:
|
|
value = raw.strip().lower()
|
|
if value == "auto":
|
|
if torch.cuda.is_available():
|
|
return torch.device("cuda")
|
|
if getattr(torch.backends, "mps", None) and torch.backends.mps.is_available():
|
|
return torch.device("mps")
|
|
return torch.device("cpu")
|
|
return torch.device(value)
|
|
|
|
|
|
def _seed(seed: int) -> None:
|
|
torch.manual_seed(seed)
|
|
if torch.cuda.is_available():
|
|
torch.cuda.manual_seed_all(seed)
|
|
|
|
|
|
def _return_scale(returns: list[float]) -> float:
|
|
values = sorted(abs(value) for value in returns if math.isfinite(value))
|
|
if not values:
|
|
return 0.0005
|
|
median = values[len(values) // 2]
|
|
mean = sum(values) / len(values)
|
|
return max(max(median, mean * 0.5), 1e-5)
|
|
|
|
|
|
def _clamp(value: float, low: float, high: float) -> float:
|
|
return max(low, min(high, value))
|
|
|
|
|
|
def _round_nested(value: Any) -> Any:
|
|
if isinstance(value, list):
|
|
return [_round_nested(item) for item in value]
|
|
return round(float(value), 10)
|
|
|
|
|
|
def _round_list(values: list[float]) -> list[float]:
|
|
return [round(float(value), 10) for value in values]
|
|
|
|
|
|
def _ints(raw: str) -> list[int]:
|
|
return [int(item.strip()) for item in raw.split(",") if item.strip()]
|
|
|
|
|
|
def _floats(raw: str) -> list[float]:
|
|
return [float(item.strip()) for item in raw.split(",") if item.strip()]
|
|
|
|
|
|
def _strings(raw: str) -> list[str]:
|
|
return [item.strip().lower() for item in raw.split(",") if item.strip()]
|
|
|
|
|
|
def _feature_names_arg(raw: str) -> list[str]:
|
|
names = [item.strip() for item in raw.split(",") if item.strip()]
|
|
return names or list(DEFAULT_TORCH_FEATURES)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|