Codex
921 lines
37 KiB
Python
921 lines
37 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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import time
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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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OUTPUT_LAYOUT = ("mean", "q10", "q50", "q90", "logit_up")
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QUANTILES = {"q10": 0.10, "q50": 0.50, "q90": 0.90}
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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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train_up: torch.Tensor
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validation_x: torch.Tensor
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validation_y: torch.Tensor
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validation_up: torch.Tensor
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validation_targets: list[list[float]]
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validation_volatility_scales: list[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_means: list[float]
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target_scales: list[float]
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target_horizons: list[int]
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decision_horizon: int
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decision_horizon_index: int
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train_samples: int
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validation_samples: int
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@dataclass(slots=True)
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class TrainingSample:
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window: list[list[float]]
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normalized_targets: list[float]
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raw_targets: list[float]
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volatility_scales: list[float]
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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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output_size: int,
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attention_pooling: bool,
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context_norm: bool,
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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.attention = nn.Linear(hidden_size, 1) if attention_pooling else None
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self.context_norm = nn.LayerNorm(hidden_size) if context_norm else nn.Identity()
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self.head = nn.Linear(hidden_size, output_size)
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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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if self.attention is not None:
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scores = self.attention(output).squeeze(-1)
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weights = torch.softmax(scores, dim=1).unsqueeze(-1)
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context = (output * weights).sum(dim=1)
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else:
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context = output[:, -1, :]
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return self.head(self.context_norm(context))
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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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decision_horizon = args.horizon if args.horizon > 0 else max(1, settings.time_series_forecast_horizon)
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target_horizons = _horizons(args.horizons, decision_horizon)
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feature_names = _feature_names_arg(args.features)
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round_trip_cost = max(0.0, 2.0 * (float(settings.taker_fee_rate) + float(settings.slippage_rate)))
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_progress(
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f"training started: symbols={len(symbols)} interval={interval} "
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f"limit={args.limit} epochs={args.epochs}"
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)
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artifact: dict[str, Any] = {
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"version": 4,
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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": decision_horizon,
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"target_horizons": target_horizons,
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"direct_horizon": True,
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"target_transform": "net_return_over_volatility",
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"target_return": "round_trip_after_cost_log_return",
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"round_trip_cost": round(round_trip_cost, 10),
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"output_layout": list(OUTPUT_LAYOUT),
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"quantiles": list(QUANTILES.values()),
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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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total_symbols = len(symbols)
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for index, symbol in enumerate(symbols, start=1):
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_progress(f"{symbol}: training started ({index}/{total_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_horizons=target_horizons,
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decision_horizon=decision_horizon,
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feature_names=feature_names,
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round_trip_cost=round_trip_cost,
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context_symbols=_strings(args.context_symbols),
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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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attention_pooling=args.attention_pooling,
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context_norm=args.context_norm,
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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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_progress(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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_progress(
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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']} horizons={','.join(map(str, result['target_horizons']))} "
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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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f"p_brier={result['probability_brier']:.4f}"
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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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_progress(f"saved {output}")
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def _progress(message: str) -> None:
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print(message, flush=True)
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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("--horizons", default="1,3,6,12", help="Comma-separated direct forecast horizons.")
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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("--context-symbols", default="BTCUSDT,ETHUSDT", help="Cross-asset context symbols.")
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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("--attention-pooling", action=argparse.BooleanOptionalAction, default=True, help="Use exportable attention pooling over recurrent states.")
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parser.add_argument("--context-norm", action=argparse.BooleanOptionalAction, default=True, help="Use exportable LayerNorm before the forecast head.")
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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_horizons: list[int],
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decision_horizon: int,
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feature_names: list[str],
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round_trip_cost: float,
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context_symbols: 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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attention_pooling: bool,
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context_norm: bool,
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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 = _historical_klines(client, 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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max_horizon = max(target_horizons)
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if len(candles) < max(180, validation_window + max(lookbacks) + max_horizon + 16):
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return None
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market_candles: dict[str, list[Candle]] = {symbol.upper(): candles}
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for context_symbol in context_symbols:
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context_symbol = context_symbol.upper()
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if context_symbol in market_candles:
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continue
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try:
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rows = _historical_klines(client, context_symbol, interval, limit)
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add_indicators(rows)
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market_candles[context_symbol] = rows
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except Exception as exc:
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_progress(f"{symbol}: context {context_symbol} skipped: {exc}")
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trend_candles = _historical_klines(client, symbol, "D", min(max(260, limit // 24 + 260), 1000))
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add_indicators(trend_candles)
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best: dict[str, Any] | None = None
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for lookback in lookbacks:
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_progress(f"{symbol}: preparing lookback={lookback}")
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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_horizons=target_horizons,
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decision_horizon=decision_horizon,
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round_trip_cost=round_trip_cost,
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market_candles=market_candles,
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trend_candles=trend_candles,
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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 = (
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sum(abs(value[prepared.decision_horizon_index]) for value in prepared.validation_targets)
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/ len(prepared.validation_targets)
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)
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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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_progress(
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f"{symbol}: fitting {architecture} "
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f"lookback={lookback} hidden={hidden_size} "
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f"layers={num_layers} dropout={dropout}"
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)
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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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output_size=len(target_horizons) * len(OUTPUT_LAYOUT),
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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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attention_pooling=attention_pooling,
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context_norm=context_norm,
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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": prepared.decision_horizon,
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"target_horizons": prepared.target_horizons,
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"direct_horizon": True,
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"target_transform": "net_return_over_volatility",
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"target_return": "round_trip_after_cost_log_return",
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"round_trip_cost": round(round_trip_cost, 10),
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"output_layout": list(OUTPUT_LAYOUT),
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"quantiles": list(QUANTILES.values()),
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"input_size": len(feature_names),
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"output_size": len(target_horizons) * len(OUTPUT_LAYOUT),
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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_means": prepared.target_means,
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"target_scales": prepared.target_scales,
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"target_mean": prepared.target_means[prepared.decision_horizon_index],
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"target_scale": prepared.target_scales[prepared.decision_horizon_index],
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"mean": prepared.target_means[prepared.decision_horizon_index],
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"scale": prepared.target_scales[prepared.decision_horizon_index],
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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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"attention_pooling": attention_pooling,
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"context_norm": context_norm,
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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_horizons: list[int],
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decision_horizon: int,
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round_trip_cost: float,
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market_candles: dict[str, list[Candle]],
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trend_candles: list[Candle],
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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(
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candles,
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feature_names,
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market_candles=market_candles,
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trend_candles=trend_candles,
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)
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max_horizon = max(target_horizons)
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samples: list[TrainingSample] = []
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for end_index in range(lookback - 1, len(candles) - max_horizon):
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current = closes[end_index]
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if current <= 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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raw_targets: list[float] = []
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volatility_scales: list[float] = []
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normalized_targets: list[float] = []
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valid = True
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for horizon in target_horizons:
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future = closes[end_index + horizon]
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if future <= 0:
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valid = False
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break
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net_return = math.log(future / current) - round_trip_cost
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volatility_scale = _target_volatility_scale(candles, closes, end_index, horizon)
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raw_targets.append(net_return)
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volatility_scales.append(volatility_scale)
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normalized_targets.append(net_return / max(volatility_scale, 1e-8))
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if valid:
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samples.append(TrainingSample(window, normalized_targets, raw_targets, volatility_scales))
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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))
|
|
train_samples = samples[:-validation_window]
|
|
validation_samples = samples[-validation_window:]
|
|
if len(train_samples) < 24 or len(validation_samples) < 8:
|
|
return None
|
|
|
|
feature_means, feature_scales = _feature_stats(train_samples, len(feature_names))
|
|
target_means, target_scales = _target_stats(train_samples, len(target_horizons))
|
|
decision_horizon = decision_horizon if decision_horizon in target_horizons else min(
|
|
target_horizons,
|
|
key=lambda value: abs(value - decision_horizon),
|
|
)
|
|
decision_horizon_index = target_horizons.index(decision_horizon)
|
|
|
|
train_x, train_y, train_up = _normalize_samples(
|
|
train_samples,
|
|
feature_means=feature_means,
|
|
feature_scales=feature_scales,
|
|
target_means=target_means,
|
|
target_scales=target_scales,
|
|
clip=clip,
|
|
)
|
|
validation_x, validation_y, validation_up = _normalize_samples(
|
|
validation_samples,
|
|
feature_means=feature_means,
|
|
feature_scales=feature_scales,
|
|
target_means=target_means,
|
|
target_scales=target_scales,
|
|
clip=clip,
|
|
)
|
|
return PreparedData(
|
|
train_x=torch.tensor(train_x, dtype=torch.float32, device=device),
|
|
train_y=torch.tensor(train_y, dtype=torch.float32, device=device),
|
|
train_up=torch.tensor(train_up, dtype=torch.float32, device=device),
|
|
validation_x=torch.tensor(validation_x, dtype=torch.float32, device=device),
|
|
validation_y=torch.tensor(validation_y, dtype=torch.float32, device=device),
|
|
validation_up=torch.tensor(validation_up, dtype=torch.float32, device=device),
|
|
validation_targets=[sample.raw_targets for sample in validation_samples],
|
|
validation_volatility_scales=[sample.volatility_scales for sample in validation_samples],
|
|
feature_names=feature_names,
|
|
feature_means=feature_means,
|
|
feature_scales=feature_scales,
|
|
target_means=target_means,
|
|
target_scales=target_scales,
|
|
target_horizons=target_horizons,
|
|
decision_horizon=decision_horizon,
|
|
decision_horizon_index=decision_horizon_index,
|
|
train_samples=len(train_x),
|
|
validation_samples=len(validation_x),
|
|
)
|
|
|
|
|
|
def _feature_stats(samples: list[TrainingSample], input_size: int) -> tuple[list[float], list[float]]:
|
|
columns = [[] for _ in range(input_size)]
|
|
for sample in samples:
|
|
window = sample.window
|
|
for row in window:
|
|
for index in range(input_size):
|
|
columns[index].append(float(row[index] if index < len(row) else 0.0))
|
|
means: list[float] = []
|
|
scales: list[float] = []
|
|
for values in columns:
|
|
if not values:
|
|
means.append(0.0)
|
|
scales.append(1.0)
|
|
continue
|
|
mean = sum(values) / len(values)
|
|
deviations = sorted(abs(value - mean) for value in values)
|
|
mad = deviations[len(deviations) // 2] if deviations else 0.0
|
|
mean_abs = sum(deviations) / len(deviations) if deviations else 0.0
|
|
means.append(mean)
|
|
scales.append(max(mad, mean_abs * 0.5, 1e-6))
|
|
return means, scales
|
|
|
|
|
|
def _target_stats(samples: list[TrainingSample], output_size: int) -> tuple[list[float], list[float]]:
|
|
means: list[float] = []
|
|
scales: list[float] = []
|
|
for index in range(output_size):
|
|
values = [sample.normalized_targets[index] for sample in samples]
|
|
mean = sum(values) / len(values) if values else 0.0
|
|
means.append(mean)
|
|
scales.append(_return_scale([value - mean for value in values]))
|
|
return means, scales
|
|
|
|
|
|
def _normalize_samples(
|
|
samples: list[TrainingSample],
|
|
*,
|
|
feature_means: list[float],
|
|
feature_scales: list[float],
|
|
target_means: list[float],
|
|
target_scales: list[float],
|
|
clip: float,
|
|
) -> tuple[list[list[list[float]]], list[list[float]], list[list[float]]]:
|
|
input_size = len(feature_means)
|
|
x_values: list[list[list[float]]] = []
|
|
y_values: list[list[float]] = []
|
|
up_values: list[list[float]] = []
|
|
for sample in samples:
|
|
window = sample.window
|
|
x_values.append(
|
|
[
|
|
[
|
|
_clamp(
|
|
((row[index] if index < len(row) else 0.0) - feature_means[index])
|
|
/ max(feature_scales[index], 1e-8),
|
|
-clip,
|
|
clip,
|
|
)
|
|
for index in range(input_size)
|
|
]
|
|
for row in window
|
|
]
|
|
)
|
|
y_values.append(
|
|
[
|
|
_clamp(
|
|
(target - target_means[index]) / max(target_scales[index], 1e-8),
|
|
-clip,
|
|
clip,
|
|
)
|
|
for index, target in enumerate(sample.normalized_targets)
|
|
]
|
|
)
|
|
up_values.append([1.0 if target > 0 else 0.0 for target in sample.raw_targets])
|
|
return x_values, y_values, up_values
|
|
|
|
|
|
def _fit_candidate(
|
|
*,
|
|
prepared: PreparedData,
|
|
architecture: str,
|
|
input_size: int,
|
|
output_size: int,
|
|
hidden_size: int,
|
|
num_layers: int,
|
|
dropout: float,
|
|
epochs: int,
|
|
patience: int,
|
|
batch_size: int,
|
|
learning_rate: float,
|
|
weight_decay: float,
|
|
clip: float,
|
|
attention_pooling: bool,
|
|
context_norm: bool,
|
|
device: torch.device,
|
|
seed: int,
|
|
) -> dict[str, Any]:
|
|
_seed(seed)
|
|
model = RecurrentReturnModel(
|
|
architecture=architecture,
|
|
input_size=input_size,
|
|
hidden_size=hidden_size,
|
|
num_layers=num_layers,
|
|
dropout=dropout,
|
|
output_size=output_size,
|
|
attention_pooling=attention_pooling,
|
|
context_norm=context_norm,
|
|
).to(device)
|
|
optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
|
|
generator = torch.Generator(device="cpu").manual_seed(seed)
|
|
loader = DataLoader(
|
|
TensorDataset(prepared.train_x, prepared.train_y, prepared.train_up),
|
|
batch_size=max(1, batch_size),
|
|
shuffle=True,
|
|
generator=generator,
|
|
)
|
|
|
|
best_state: dict[str, torch.Tensor] | None = None
|
|
best_metrics: dict[str, float] = {"validation_mae": math.inf, "directional_accuracy": 0.0, "buy_precision": 0.0}
|
|
best_epoch = 0
|
|
stale_epochs = 0
|
|
for epoch in range(1, max(1, epochs) + 1):
|
|
model.train()
|
|
for batch_x, batch_y, batch_up in loader:
|
|
optimizer.zero_grad(set_to_none=True)
|
|
loss = _forecast_loss(model(batch_x), batch_y, batch_up, len(prepared.target_horizons))
|
|
loss.backward()
|
|
nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
|
|
optimizer.step()
|
|
|
|
metrics = _validation_metrics(model, prepared, clip)
|
|
if metrics["validation_mae"] + 1e-12 < best_metrics["validation_mae"]:
|
|
best_metrics = metrics
|
|
best_epoch = epoch
|
|
best_state = {key: value.detach().cpu().clone() for key, value in model.state_dict().items()}
|
|
stale_epochs = 0
|
|
else:
|
|
stale_epochs += 1
|
|
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_nested(model.head.weight.detach().cpu().tolist()),
|
|
"head_bias": _round_list(model.head.bias.detach().cpu().tolist()),
|
|
**_export_context_state(model),
|
|
}
|
|
|
|
|
|
def _validation_metrics(model: nn.Module, prepared: PreparedData, clip: float) -> dict[str, float]:
|
|
model.eval()
|
|
with torch.no_grad():
|
|
raw_outputs = model(prepared.validation_x).detach().cpu()
|
|
outputs = raw_outputs.view(len(prepared.validation_targets), len(prepared.target_horizons), len(OUTPUT_LAYOUT))
|
|
mean_predictions = outputs[:, :, 0].tolist()
|
|
logit_predictions = outputs[:, :, 4].tolist()
|
|
predictions: list[list[float]] = []
|
|
probabilities: list[list[float]] = []
|
|
for row_index, row in enumerate(mean_predictions):
|
|
predicted_row: list[float] = []
|
|
probability_row: list[float] = []
|
|
for horizon_index, normalized_prediction in enumerate(row):
|
|
transformed = (
|
|
_clamp(float(normalized_prediction), -clip, clip)
|
|
* prepared.target_scales[horizon_index]
|
|
+ prepared.target_means[horizon_index]
|
|
)
|
|
predicted_row.append(transformed * prepared.validation_volatility_scales[row_index][horizon_index])
|
|
probability_row.append(_sigmoid(float(logit_predictions[row_index][horizon_index])))
|
|
predictions.append(predicted_row)
|
|
probabilities.append(probability_row)
|
|
decision = prepared.decision_horizon_index
|
|
decision_predictions = [row[decision] for row in predictions]
|
|
decision_targets = [row[decision] for row in prepared.validation_targets]
|
|
errors = [abs(prediction - actual) for prediction, actual in zip(decision_predictions, decision_targets)]
|
|
correct = [
|
|
1.0
|
|
for prediction, actual in zip(decision_predictions, decision_targets)
|
|
if (prediction > 0 and actual > 0) or (prediction < 0 and actual < 0)
|
|
]
|
|
non_zero = [
|
|
1.0
|
|
for prediction, actual in zip(decision_predictions, decision_targets)
|
|
if prediction != 0 and actual != 0
|
|
]
|
|
buy_predictions = [
|
|
actual
|
|
for prediction, actual in zip(decision_predictions, decision_targets)
|
|
if prediction > 0
|
|
]
|
|
buy_wins = [actual for actual in buy_predictions if actual > 0]
|
|
by_horizon = {}
|
|
baseline_by_horizon = {}
|
|
for horizon_index, horizon in enumerate(prepared.target_horizons):
|
|
horizon_errors = [
|
|
abs(row[horizon_index] - actual[horizon_index])
|
|
for row, actual in zip(predictions, prepared.validation_targets)
|
|
]
|
|
horizon_baseline = [abs(actual[horizon_index]) for actual in prepared.validation_targets]
|
|
by_horizon[str(horizon)] = sum(horizon_errors) / len(horizon_errors) if horizon_errors else math.inf
|
|
baseline_by_horizon[str(horizon)] = (
|
|
sum(horizon_baseline) / len(horizon_baseline)
|
|
if horizon_baseline
|
|
else math.inf
|
|
)
|
|
probability_errors = [
|
|
(probabilities[row_index][decision] - (1.0 if target > 0 else 0.0)) ** 2
|
|
for row_index, target in enumerate(decision_targets)
|
|
]
|
|
return {
|
|
"validation_mae": sum(errors) / len(errors) if errors else math.inf,
|
|
"validation_mae_by_horizon": by_horizon,
|
|
"baseline_mae_by_horizon": baseline_by_horizon,
|
|
"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,
|
|
"probability_brier": sum(probability_errors) / len(probability_errors) if probability_errors else 1.0,
|
|
}
|
|
|
|
|
|
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))
|
|
probability_brier = float(row.get("probability_brier", 1.0))
|
|
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
|
|
) * (1.0 + max(0.0, probability_brier - 0.25) * 0.02)
|
|
|
|
|
|
def _forecast_loss(outputs: torch.Tensor, targets: torch.Tensor, up_targets: torch.Tensor, horizon_count: int) -> torch.Tensor:
|
|
values = outputs.view(outputs.shape[0], horizon_count, len(OUTPUT_LAYOUT))
|
|
mean_loss = nn.functional.smooth_l1_loss(values[:, :, 0], targets, beta=0.5)
|
|
quantile_losses = []
|
|
for offset, name in enumerate(("q10", "q50", "q90"), start=1):
|
|
quantile = QUANTILES[name]
|
|
errors = targets - values[:, :, offset]
|
|
quantile_losses.append(torch.maximum((quantile - 1.0) * errors, quantile * errors).mean())
|
|
logits = values[:, :, 4]
|
|
bce = nn.functional.binary_cross_entropy_with_logits(logits, up_targets, reduction="none")
|
|
probabilities = torch.sigmoid(logits)
|
|
pt = probabilities * up_targets + (1.0 - probabilities) * (1.0 - up_targets)
|
|
focal = ((1.0 - pt) ** 2.0 * bce).mean()
|
|
return mean_loss + 0.35 * sum(quantile_losses) / len(quantile_losses) + 0.15 * focal
|
|
|
|
|
|
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 _export_context_state(model: RecurrentReturnModel) -> dict[str, Any]:
|
|
exported: dict[str, Any] = {}
|
|
if model.attention is not None:
|
|
exported["attention_pooling"] = True
|
|
exported["attention_weight"] = _round_list(model.attention.weight.detach().cpu().squeeze(0).tolist())
|
|
exported["attention_bias"] = round(float(model.attention.bias.detach().cpu().item()), 10)
|
|
else:
|
|
exported["attention_pooling"] = False
|
|
if isinstance(model.context_norm, nn.LayerNorm):
|
|
exported["context_norm"] = True
|
|
exported["context_norm_weight"] = _round_list(model.context_norm.weight.detach().cpu().tolist())
|
|
exported["context_norm_bias"] = _round_list(model.context_norm.bias.detach().cpu().tolist())
|
|
else:
|
|
exported["context_norm"] = False
|
|
return exported
|
|
|
|
|
|
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 _target_volatility_scale(candles: list[Candle], closes: list[float], end_index: int, horizon: int) -> float:
|
|
horizon = max(1, horizon)
|
|
close = max(closes[end_index], 1e-12)
|
|
candle = candles[end_index]
|
|
atr_scale = (candle.atr_14 / close) * math.sqrt(horizon) if candle.atr_14 is not None else 0.0
|
|
start = max(1, end_index - 96)
|
|
returns = [
|
|
math.log(closes[index] / closes[index - 1])
|
|
for index in range(start, end_index + 1)
|
|
if closes[index] > 0 and closes[index - 1] > 0
|
|
]
|
|
realized = math.sqrt(sum(value * value for value in returns) / len(returns)) * math.sqrt(horizon) if returns else 0.0
|
|
return max(atr_scale * 0.7, realized, 0.0005)
|
|
|
|
|
|
def _historical_klines(client: BybitClient, symbol: str, interval: str, limit: int) -> list[Candle]:
|
|
limit = max(1, limit)
|
|
rows_by_timestamp: dict[int, Candle] = {}
|
|
end: int | None = None
|
|
while len(rows_by_timestamp) < limit:
|
|
page_limit = min(1000, limit - len(rows_by_timestamp))
|
|
params: dict[str, Any] = {
|
|
"category": "spot",
|
|
"symbol": symbol,
|
|
"interval": interval,
|
|
"limit": page_limit,
|
|
}
|
|
if end is not None:
|
|
params["end"] = end
|
|
result = client.public_get("/v5/market/kline", params)
|
|
page = _parse_kline_rows(result.get("list", []))
|
|
if not page:
|
|
break
|
|
for candle in page:
|
|
rows_by_timestamp[candle.timestamp] = candle
|
|
oldest = min(candle.timestamp for candle in page)
|
|
if end is not None and oldest >= end:
|
|
break
|
|
end = oldest - 1
|
|
if len(page) < page_limit:
|
|
break
|
|
time.sleep(0.05)
|
|
return sorted(rows_by_timestamp.values(), key=lambda item: item.timestamp)[-limit:]
|
|
|
|
|
|
def _parse_kline_rows(rows: Any) -> list[Candle]:
|
|
candles: list[Candle] = []
|
|
for row in rows or []:
|
|
if len(row) < 7:
|
|
continue
|
|
candles.append(
|
|
Candle(
|
|
timestamp=int(row[0]),
|
|
open=_float(row[1]),
|
|
high=_float(row[2]),
|
|
low=_float(row[3]),
|
|
close=_float(row[4]),
|
|
volume=_float(row[5]),
|
|
turnover=_float(row[6]),
|
|
)
|
|
)
|
|
candles.sort(key=lambda item: item.timestamp)
|
|
return candles
|
|
|
|
|
|
def _float(value: Any, default: float = 0.0) -> float:
|
|
try:
|
|
return float(value)
|
|
except (TypeError, ValueError):
|
|
return default
|
|
|
|
|
|
def _clamp(value: float, low: float, high: float) -> float:
|
|
return max(low, min(high, value))
|
|
|
|
|
|
def _sigmoid(value: float) -> float:
|
|
if value >= 40:
|
|
return 1.0
|
|
if value <= -40:
|
|
return 0.0
|
|
return 1 / (1 + math.exp(-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]:
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return [float(item.strip()) for item in raw.split(",") if item.strip()]
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|
|
|
|
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def _strings(raw: str) -> list[str]:
|
|
return [item.strip().lower() for item in raw.split(",") if item.strip()]
|
|
|
|
|
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def _horizons(raw: str, decision_horizon: int) -> list[int]:
|
|
values = []
|
|
for value in _ints(raw or ""):
|
|
if 1 <= value <= 96 and value not in values:
|
|
values.append(value)
|
|
decision_horizon = max(1, min(96, int(decision_horizon)))
|
|
if decision_horizon not in values:
|
|
values.append(decision_horizon)
|
|
values.sort()
|
|
return values
|
|
|
|
|
|
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()
|