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🔍 Problem Statement..

Predict the next-minute stock return (ratio of time T+1 price to time T price) using market microstructure features such as volume, trade imbalance, spreads, and order book statistics.

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Features Engineered

Aggregated per minute per stock:

• num_trades, o, h, l, c, total_volume,
• total_buy_cap, total_sell_cap, weighted_price
• trade_imbalance_ratio, volume_imbalance_ratio
• avg_spread, max_spread, min_spread
• weighted_avg_bid_price, weighted_avg_ask_price

Target variable:

• Return = (Next minute's close price) / (Current minute's close price)

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Work

• Aggregated trade and quote data to 1-minute intervals
• Engineered predictive features
• Modeled using Linear Regression
• Evaluated with:
• Minute-wise correlation plot (actual vs predicted returns)
• Correlation matrix heatmap across all companies

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📊 Example Outputs

1. Correlation over time

Measures how well predictions match real returns over each minute.

2. Correlation Matrix

Stock-wise return correlations across companies on the selected date.

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