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README.md

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 language:
 - en
 pipeline_tag: time-series-forecasting
----
+---
+
+
+# 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)
+
+![Prediction vs True Value](./prediction_vs_true.png)
+
+### 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
+
+
+---
+
+## 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!
+
+---