multiasset-market-making-model

MultiAsset Market Making Model: Transformer based Interval Forecasting

Model Summary & Evaluation

Overview

This project implements a transformer based time-series forecasting model for 5-minute OHLCV data on Binance BTC Perp and OKX BTC Spot. The model uses cross-channel attention and a custom interval score loss to predict high/low ranges for both assets. Model achieves 47.35% (BTC Perp) and 74.43% (BTC Spot) interval coverage accuracy with lambda_width=5.

Prediction vs True Value (Visualization)

Technical Foundation

• Python (pandas, matplotlib, mplfinance): Data download, cleaning, plotting
• PyTorch: Model architecture, training, evaluation
• MultiheadAttention (5 heads): Cross-channel attention for multi asset modeling
• Interval Score Loss (lambda_width=5): Penalizes missed intervals and wide ranges, differentiable for training
• Data alignment: Merging Binance and OKX OHLCV by timestamp

Codebase Status

• main.py: Trains model on combined Binance/OKX data, interval score loss, outputs 4 values: BTC Perp high/low and BTC Spot high/low
• test_only.py: Plots true/pred candlesticks for both exchanges, computes relaxed (OR) interval accuracy

Problem Resolution

• Issues: Data ordering, deduplication, plotting errors, shape mismatches, loss function encouraging wide intervals
• Solutions: Batch reversal, sorting, deduplication, robust CSV reading, interval score loss with width penalty, OR logic for interval accuracy
• Lessons Learned: Interval score loss must balance coverage and precision; width penalty is critical to avoid trivial solutions

Progress Tracking

• Completed: Data download/cleaning, plotting, model implementation, training, evaluation, documentation
• Recent Context: Summary and evaluation based on latest training/test results and loss function
• Pending: Further model tuning (lambda_width, architecture), additional evaluation metrics or visualizations, publishing code/model/dataset

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Disclaimer

Note: This repository is a work in progress. The model weights, code, and dataset will be published in a later stage. Current release includes only documentation, summary, and evaluation logs. Stay tuned for updates!

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