You trade E-mini S&P 500 futures. The price stays in a narrow range for 15 minutes, but the order book volume grows. Regular indicators say nothing, while the Order Flow model sees: buyer volume aggressively exceeds seller volume, CVD is rising, the Footprint shows clusters at the 4500 level. Two minutes later, the price breaks the level from below — you know in advance it is a false breakout. Our AI Order Flow analysis model provides this advantage. Solutions are already deployed in 10+ projects, and our team has over 5 years of experience in AI/ML for financial markets.
How Does Aggressor Classification Work?
Every trade has an initiator — the aggressive side. According to the Lee-Ready algorithm, proposed over 30 years ago: a trade at a price higher than the previous is buyer-initiated, lower is seller-initiated. If the price is unchanged, we look at the previous movement. We fine-tune the classifier on labeled data, achieving up to 95% accuracy for cryptocurrencies (Binance) and 92% for futures (CME).
What Are Delta and CVD?
CVD (Cumulative Volume Delta) is a key Order Flow indicator:
Delta = Buyer_Volume - Seller_Volume CVD = Σ Delta over period Positive CVD with rising price = trend confirmation. Negative CVD with rising price = divergence, often preceding a reversal. We build CVD for multiple time windows (1s, 30s, 5min) and feed it as a feature into the model. Additionally, Absorption: when a large player absorbs aggressive orders without price movement. These are support/resistance levels that the model learns to detect.
Feature Engineering: From Ticks to Features
Tick data is transformed into features via rolling window aggregation:
def compute_order_flow_features(trades_df, window_seconds=60): features = {} trades_df['initiator'] = np.where(trades_df['side'] == 'buy', 1, -1) features['buy_volume'] = trades_df[trades_df.initiator==1]['volume'].rolling(f'{window_seconds}s').sum() features['sell_volume'] = trades_df[trades_df.initiator==-1]['volume'].rolling(f'{window_seconds}s').sum() features['cvd'] = features['buy_volume'] - features['sell_volume'] features['trade_imbalance'] = features['cvd'] / (features['buy_volume'] + features['sell_volume']) features['avg_buy_size'] = (features['buy_volume'] / buy_count) features['avg_sell_size'] = (features['sell_volume'] / sell_count) features['large_buy_ratio'] = (large_buy_volume / total_volume) return features Volume Profile — a histogram of volume at price levels. VPOC (Volume Point of Control) — level with maximum volume, used as support/resistance. Time and Sales analysis: clusters of large trades in a short time = large player entering a position.
Footprint CNN: When a Neural Network Reads Clusters
A Footprint Chart (Cluster Chart) combines Order Book and Order Flow: each candle is split into price levels, each level contains [buyer_volume × seller_volume]. We feed this data as a 3D tensor [time_bins × price_levels × 2] into a convolutional network:
class FootprintCNN(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv3d(1, 32, kernel_size=(3, 3, 2)) self.conv2 = nn.Conv3d(32, 64, kernel_size=(3, 3, 1)) self.flatten = nn.Flatten() self.fc = nn.Linear(64 * ..., 1) The CNN learns to detect divergences and absorption that are inaccessible to linear models.
Comparison of Approaches: ML vs Classic Indicators
| Method | Accuracy (1-min forecast) | Training Time | Interpretability |
|---|---|---|---|
| Linear regression on CVD | 55-60% | 1 hour | High |
| Gradient Boosting | 60-65% | 2-4 hours | Medium |
| Footprint CNN | 65-70% | 1-2 days (GPU) | Low |
The CNN on footprint data outperforms regular technical indicators by 2-3 times in price direction prediction accuracy on short intervals.
Accuracy Comparison by Instrument (1-minute forecast)
| Instrument | Linear Regression | Gradient Boosting | Footprint CNN |
|---|---|---|---|
| E-mini S&P 500 | 57% | 63% | 68% |
| BTC/USD | 55% | 61% | 66% |
| EUR/USD | 59% | 64% | 70% |
The CNN consistently outperforms classic ML models on all instruments, especially on volatile markets.
AI Model Deployment Process
- Data audit — assess tick data quality, select source, calculate required volume (minimum 3 months of history).
- Feature Engineering — develop Order Flow, Volume Profile, Stacked Imbalance features.
- Baseline model — Linear Regression or LightGBM for a quick baseline (3-4 weeks).
- Advanced model — Footprint CNN with backtesting and optimization (3-4 months).
- MLOps Pipeline — deploy model to production (Kubernetes, vLLM, Kafka for streaming).
- Monitoring and drift — automatic quality reassessment, alerting on metric drops.
What Is Included in the Result
- Source code of the model and pipelines (Python, PyTorch/TensorFlow).
- Documentation: architecture description, run instructions, API specification.
- Team training (2-3 sessions of 4 hours each).
- Technical support for 3 months after deployment.
- Guarantee: if the model does not meet target metrics (ROC-AUC ≥ 0.7 on validation), we refine it free of charge.
Estimated Timelines
- Order Flow Feature Engineering + baseline regression: 3-4 weeks.
- Footprint CNN with backtesting and production pipeline: 3-4 months.
- The cost is calculated individually after a data audit and defined KPIs. Contact us for a consultation — we will select the optimal solution for your infrastructure and budget. Order a pilot project: we will conduct a data audit and show a baseline model in 2 weeks.