Custom AI Model for Asset Price Prediction

Why Most AI Price Forecasting Models Fail?

Traders and funds often invest resources in developing complex models, but on real markets they show losses. The reason lies in three traps: look-ahead bias, ignoring transaction costs, and overfitting to historical data. For example, a simple moving average model may show a Sharpe ratio of 1.5 in backtest, but in live trading without accounting for slippage and commissions, this drops to 0.3. We have encountered projects where a model with IC 0.08 on validation showed Sharpe 0.2 in production — due to ignoring slippage. Our approach eliminates such surprises. We solve these issues through purged walk-forward validation, a realistic transaction cost model (Almgren-Chriss), and strict factor selection control.

How to Choose the Forecasting Horizon?

The practical goal is not the exact price in N days, but a signal with positive expected value after transaction costs. Even a model with MAPE 3% on S&P500 stocks is useless if the strategy's Sharpe ratio is < 0. The horizon determines the signal type:

• Intraday (minutes-hours): microstructure signals, order flow imbalance — typical return 0.5–1.5% per trade.
• Short-term (1-5 days): momentum, mean reversion — average IC 0.05–0.08.
• Medium-term (1-4 weeks): earnings, macro catalysts — IC can reach 0.12.
• Long-term (months): fundamental valuation, factor exposure — more stable but requires higher accuracy.

The optimal horizon depends on the instrument's liquidity and rebalancing frequency. For less liquid assets, shorter horizons are less reliable.

What is Purged Walk-Forward Validation?

Correct validation is key to a realistic backtest. We use purged walk-forward cross-validation:

• Training: t=0 to t=T
• Purge gap: T to T+embargo (eliminates look-ahead from overlapping labels)
• Test: T+embargo to T+embargo+H
• Embargo period: usually equal to the forecast horizon

Embargo period ensures that future information does not leak into the training set. This is critical for time series. Metrics: IC (Information Coefficient) — correlation between predicted and actual return ranks. IC > 0.05 is weak, IC > 0.10 is good. ICIR (IC Information Ratio) — signal stability. Strategy Sharpe ratio from the signal is the main practical metric. Efficient Market Hypothesis states markets are efficient, but in practice micro-anomalies exist and can be identified with correct validation.

For model selection, consider the volume and structure of data: if many instruments — LightGBM ranking, if a single time series — LSTM, if multi-instruments with known events — Temporal Fusion Transformer. With limited data, start with LightGBM.

Model Features and Architecture

Price-based (technical analysis):

• Returns: log returns for 1, 5, 10, 21 trading days.
• Momentum: 12-1 month momentum (Jegadeesh-Titman factor).
• RSI, MACD, Bollinger Band width — oscillators as functions of price.
• Volatility: realized volatility for 5/21/63 days.

Volume-based:

• Volume relative to 20-day average.
• Price × Volume (dollar volume).
• On-Balance Volume (OBV).
• VWAP deviation.

Fundamental (for stocks):

• P/E, P/B, EV/EBITDA.
• EPS growth YoY.
• Revenue growth.
• Debt/Equity.

Alternative data:

• Sentiment from Twitter/Reddit (NLP score).
• Google Trends for consumer stocks.
• Satellite imagery (retail parking lots, commodity stores).
• Job postings growth (Glassdoor, LinkedIn).

Comparison of main modeling approaches:

LightGBM trains 10x faster than LSTM on tabular data — an advantage for rapid prototyping. For ranking tasks, we use LGBMRanker with objective='lambdarank'. Example configuration:

import lightgbm as lgb
model = lgb.LGBMRanker(
    objective='lambdarank',
    n_estimators=500,
    learning_rate=0.05,
    max_depth=6
)

For single instrument time series, we use LSTM with 60 days of history:

model = Sequential([
    LSTM(64, return_sequences=True, input_shape=(60, n_features)),
    Dropout(0.2),
    LSTM(32),
    Dropout(0.2),
    Dense(1)
])

Temporal Fusion Transformer — the best choice when known future covariates (earnings dates, macro events) and 100+ instruments are available.

Model quality is assessed not only by IC. We use a comprehensive set of metrics:

From Model to Trading Strategy

Model → signal → position → PnL — a chain with multiple stages of loss:

1. Signal generation: ranking score across stock universe (typically 500-1000 instruments).
2. Portfolio construction: mean-variance optimization (Markowitz) or equal-weight deciles. Typical number of positions 20-50.
3. Risk management: limits on sector/factor exposure, max position size 5%.
4. Transaction cost model: bid-ask spread + market impact (Almgren-Chriss) — accounts for slippage, often 10-30 bps.
5. Backtesting: with real TC and slippage — key! We use Zipline / Backtrader or custom backtester.

Common mistakes: survivorship bias (training only on existing stocks), look-ahead bias in fundamental data (use point-in-time), ignoring transaction costs. We document every assumption.

What's Included

• Documentation: dashboard with metrics (IC, Sharpe), model description, reproducible code.
• Model access: REST API or Python package with documentation.
• Team training: workshop on operation and retraining.
• Support: 3 months after deployment, including drift monitoring.

Get an engineer consultation for your project — we'll assess data and timelines.

Our Results

We have built models for several hedge funds and prop trading teams. Average savings on transaction costs are 20-30% compared to naive benchmarks. We guarantee IC > 0.05 on out-of-sample, Sharpe ratio > 1.0 after TC. Certified in AWS and GCP ML. Experience with LightGBM and PyTorch. Contact us for a project evaluation — we'll calculate timelines and cost.