LSTM for Financial Time Series: Architecture and Validation

import torch
import torch.nn as nn

class FinancialLSTM(nn.Module):
    def __init__(self, input_size, hidden_size=128, num_layers=2, dropout=0.2):
        super().__init__()
        self.lstm = nn.LSTM(
            input_size=input_size,
            hidden_size=hidden_size,
            num_layers=num_layers,
            batch_first=True,
            dropout=dropout
        )
        self.attention = nn.MultiheadAttention(hidden_size, num_heads=8)
        self.fc = nn.Linear(hidden_size, 1)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        lstm_out, _ = self.lstm(x)  # [batch, seq_len, hidden]
        # Self-attention over time dimension
        attn_out, _ = self.attention(lstm_out, lstm_out, lstm_out)
        # Last step or attention-weighted pool
        out = self.fc(self.dropout(attn_out[:, -1, :]))
        return out

Input data (seq_len × n_features): OHLCV, normalized by rolling window, technical indicators (RSI, MACD, ATR, Bollinger). For multi-asset — concatenation along feature dimension. Implementation is available in PyTorch LSTM.

Preprocessing and Normalization

Critically important: normalization without lookahead bias. We use rolling window normalization:

def rolling_normalize(X, window=252):
    mu = X.rolling(window).mean()
    sigma = X.rolling(window).std()
    return (X - mu) / (sigma + 1e-8)

Price returns instead of prices: raw prices are non-stationary, log returns are stationary:

returns = np.log(prices / prices.shift(1)).dropna() 

Sequence generation:

def create_sequences(data, seq_len=60, horizon=5):
    X, y = [], []
    for i in range(len(data) - seq_len - horizon):
        X.append(data[i:i+seq_len])
        y.append(data[i+seq_len+horizon-1, 0])
    return np.array(X), np.array(y)

Training and Regularization

How to tune hyperparameters for financial LSTMs?

Sequence length: 20–60 days for daily data, 50–200 for hourly. Hidden size: 64–256. Layers: 2–3 (deeper is usually worse on financial data). Dropout: 0.1–0.4. Batch size: 32–128. Regularization: temporal dropout, feature noise, L2 weight decay (1e-4 to 1e-3). Optimizer: AdamW with cosine annealing LR scheduler. Early stopping on validation loss on a 20% holdout.

For a portfolio of N instruments, we use Cross-sectional LSTM with parallel processing of all instruments and cross-attention between them to capture correlation patterns (oil → oil stocks, DXY → EM assets).

Validation Without Data Leakage

Walk-forward with embargo:

embargo_size = horizon
train_end = int(0.6 * len(data))
embargo_end = train_end + embargo_size
val_end = int(0.8 * len(data))

Metrics: Directional Accuracy, Information Coefficient (spearman correlation), ICIR (IC / std(IC) — stability; ICIR > 1.5 is considered good).

Comparison of Normalization Methods

LSTM vs Transformer for Finance

For short sequences (< 100 steps), LSTM often matches Transformer with significantly less data requirements.

What Is Included in the Work

• Baseline single-asset model with built pipeline and documentation
• Multi-asset architecture with cross-attention and walk-forward validation
• Hyperparameter optimization (Optuna) with logs in MLflow
• Docker deployment with Triton Inference Server and monitoring in Prometheus
• Training of the operations team and handover of the model card

Each stage is accompanied by reports and code comments. We don't just deliver weights — we hand over a reproducible experiment.

Process and Timelines

• Analysis — data collection and visualization, defining the forecast horizon.
• Design — selection of architecture (LSTM/Transformer, single/multi-asset).
• Implementation — writing pipeline, training baseline, optimization.
• Testing — walk-forward validation, stress testing on anomalies.
• Deployment — packaging in Docker, deploying on a GPU server, monitoring.

Timelines: single-asset baseline — 2 to 3 weeks; multi-asset model with attention and production pipeline — 8 to 10 weeks. Cost is calculated individually.

Request a consultation for a preliminary assessment of your dataset — we will analyze it in 1–2 days. Contact us to discuss the model architecture and timelines.