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d5789cd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 | import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset, DataLoader
from typing import Optional, Tuple, List, Dict
def compute_technical_indicators(df):
df = df.copy()
df['ret'] = df['close'].pct_change()
df['log_ret'] = np.log(df['close'] / df['close'].shift(1))
df['volatility_5'] = df['ret'].rolling(5).std()
df['volatility_20'] = df['ret'].rolling(20).std()
df['ma_5'] = df['close'].rolling(5).mean()
df['ma_20'] = df['close'].rolling(20).mean()
delta = df['close'].diff()
gain = delta.where(delta > 0, 0)
loss = -delta.where(delta < 0, 0)
avg_gain = gain.rolling(14).mean()
avg_loss = loss.rolling(14).mean()
rs = avg_gain / avg_loss
df['rsi'] = 100 - (100 / (1 + rs))
ema_12 = df['close'].ewm(span=12).mean()
ema_26 = df['close'].ewm(span=26).mean()
df['macd'] = ema_12 - ema_26
df['macd_signal'] = df['macd'].ewm(span=9).mean()
df['vol_ma_5'] = df['volume'].rolling(5).mean()
df['volume_ratio'] = df['volume'] / df['vol_ma_5']
high_low = df['high'] - df['low']
high_close = np.abs(df['high'] - df['close'].shift())
low_close = np.abs(df['low'] - df['close'].shift())
tr = pd.concat([high_low, high_close, low_close], axis=1).max(axis=1)
df['atr'] = tr.rolling(14).mean()
df = df.fillna(0)
return df
def normalize_features(arr):
mean = arr.mean(axis=0, keepdims=True)
std = arr.std(axis=0, keepdims=True) + 1e-6
return (arr - mean) / std
class FinancialTrajectoryDataset(Dataset):
def __init__(self, data, n_assets=1, context_window=60, target_window=5,
feature_cols=None, stride=1, normalize=True):
self.data = data.reset_index(drop=True)
self.n_assets = n_assets
self.context_window = context_window
self.target_window = target_window
self.stride = stride
self.normalize = normalize
if feature_cols is None:
feature_cols = ['open', 'high', 'low', 'close', 'volume', 'ret', 'log_ret',
'volatility_5', 'volatility_20', 'rsi', 'macd', 'macd_signal',
'volume_ratio', 'atr']
self.feature_cols = [c for c in feature_cols if c in self.data.columns]
self.n_features = len(self.feature_cols)
self.features = self.data[self.feature_cols].values.astype(np.float32)
if normalize:
self.features = normalize_features(self.features)
self.returns = self.data['ret'].values.astype(np.float32)
self.total_len = len(self.data)
self.indices = list(range(0, self.total_len - context_window - target_window, stride))
def __len__(self):
return len(self.indices)
def __getitem__(self, idx):
start = self.indices[idx]
ctx_end = start + self.context_window
tgt_end = ctx_end + self.target_window
context = self.features[start:ctx_end]
target = self.features[ctx_end:tgt_end]
future_ret = self.returns[ctx_end:tgt_end]
avg_ret = future_ret.mean() if len(future_ret) > 0 else 0.0
if self.n_assets == 1:
weights = np.array([1.0], dtype=np.float32)
else:
weights = np.random.dirichlet(np.ones(self.n_assets)).astype(np.float32)
if avg_ret > 0.01:
signal = 0
elif avg_ret < -0.01:
signal = 1
else:
signal = 2
signals = np.array([signal] * self.n_assets, dtype=np.int64)
hedge = 0
return {
"context": torch.from_numpy(context),
"target": torch.from_numpy(target),
"weights": torch.from_numpy(weights),
"signals": torch.from_numpy(signals),
"hedge": torch.tensor(hedge, dtype=torch.long),
}
def build_dataloaders(data, n_assets=1, context_window=60, target_window=5,
batch_size=64, train_ratio=0.8, val_ratio=0.1, num_workers=0):
n = len(data)
train_end = int(n * train_ratio)
val_end = int(n * (train_ratio + val_ratio))
train_data = data.iloc[:train_end]
val_data = data.iloc[train_end:val_end]
test_data = data.iloc[val_end:]
train_ds = FinancialTrajectoryDataset(train_data, n_assets, context_window, target_window)
val_ds = FinancialTrajectoryDataset(val_data, n_assets, context_window, target_window)
test_ds = FinancialTrajectoryDataset(test_data, n_assets, context_window, target_window)
return {
"train": DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=num_workers, drop_last=True),
"val": DataLoader(val_ds, batch_size=batch_size, shuffle=False, num_workers=num_workers, drop_last=True),
"test": DataLoader(test_ds, batch_size=batch_size, shuffle=False, num_workers=num_workers, drop_last=True),
}
def load_hf_stock_data(dataset_name="paperswithbacktest/Stocks-Daily-Price", symbols=None, max_rows=100_000):
try:
from datasets import load_dataset
ds = load_dataset(dataset_name, split="train", streaming=True)
rows = []
for i, row in enumerate(ds):
if i >= max_rows:
break
if symbols is not None and row["symbol"] not in symbols:
continue
rows.append({
"symbol": row["symbol"],
"date": row["date"],
"open": row["open"],
"high": row["high"],
"low": row["low"],
"close": row["close"],
"volume": row["volume"],
"adj_close": row.get("adj_close", row["close"]),
})
df = pd.DataFrame(rows)
df = compute_technical_indicators(df)
return df
except Exception as e:
print(f"Error loading HF dataset: {e}")
return generate_synthetic_data(n_timesteps=max_rows, n_assets=1 if symbols is None else len(symbols))
def generate_synthetic_data(n_timesteps=5000, n_assets=1, seed=42):
np.random.seed(seed)
price = 100.0
data = []
for t in range(n_timesteps):
ret = np.random.normal(0.0002, 0.02)
price *= (1 + ret)
high = price * (1 + abs(np.random.normal(0, 0.005)))
low = price * (1 - abs(np.random.normal(0, 0.005)))
open_p = price * (1 + np.random.normal(0, 0.003))
vol = int(np.random.lognormal(15, 0.5))
data.append({
"open": open_p, "high": high, "low": low, "close": price,
"volume": vol, "adj_close": price,
})
df = pd.DataFrame(data)
df = compute_technical_indicators(df)
return df
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