Create README.mdREADME.md

README.mdREADME.md

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+# Time-Series-Crypto-Transformer
+
+## Overview
+`Time-Series-Crypto-Transformer` is a dedicated encoder-decoder Transformer model optimized for multivariate time series forecasting, specifically predicting the 7-day future closing price trend for major cryptocurrencies. The model incorporates both temporal features (price, volume, volatility) and static categorical features (Asset ID) to generate robust predictions.
+
+This model is a custom implementation based on the architecture popularized by models like Informer and Autoformer, adapted for the financial domain's low-latency requirements.
+
+## Model Architecture
+The model uses a **Transformer Encoder-Decoder** structure.
+* **Architecture:** Custom Transformer for Time Series (built using `HuggingFace-TS` principles).
+* **Input Features (`input_size`=12):** [Close Price (Target), Open, High, Low, Volume, Log Returns, Moving Averages (2), Volatility (2), Time-of-Day, Day-of-Week].
+* **Context Length (`context_length`):** 90 time steps (equivalent to 90 days of look-back).
+* **Prediction Length (`prediction_length`):** 7 time steps (7-day forecast).
+* **Dimensionality:** $d_{model}=256$, $n_{layers}=4$ (Encoder & Decoder), $n_{heads}=8$.
+* **Static Features:** Asset ID (5 major cryptocurrencies: BTC, ETH, BNB, SOL, ADA).
+
+## Intended Use
+* **7-Day Price Trend Forecasting:** Predicting the direction and magnitude of the close price for the next week.
+* **Algorithmic Trading Signals:** Integrating forecasts into a larger trading system for entry/exit points.
+* **Risk Management:** Quantifying future price volatility to manage portfolio risk exposures.
+* **Benchmarking:** Serving as a strong baseline model for comparison with other time-series models (e.g., ARIMA, LSTMs) in the crypto domain.
+
+## Limitations
+* **Black Swan Events:** The model relies on historical patterns and may fail to accurately predict sudden, high-impact events (e.g., major regulatory changes, exchange failures) that are not represented in the training data.
+* **Data Stationarity:** Performance assumes a certain degree of non-stationarity in the time series, but extreme shifts in market structure may degrade accuracy.
+* **Feature Dependence:** Accuracy is highly dependent on the quality and preprocessing of the 12 input features. Missing or noisy data will significantly impact results.
+
+## Example Code (Python - Conceptual)
+
+```python
+import pandas as pd
+from transformers import AutoConfig, TimeSeriesModel
+
+model_name = "Quant/Time-Series-Crypto-Transformer"
+config = AutoConfig.from_pretrained(model_name)
+model = TimeSeriesModel(config)
+
+# --- Conceptual Data Preparation ---
+# Your input data should be a 3D tensor: [Batch Size, Context Length, Input Size (Features)]
+# Example: 
+# Historical data for 90 days (context_length) of 5 assets (batch size 5) with 12 features (input_size)
+historical_data = torch.randn(5, config.context_length, config.input_size)
+static_features = torch.tensor([0, 1, 2, 3, 4]) # Asset IDs
+
+# --- Inference ---
+# The forward pass returns the distribution or point estimate for the prediction_length (7 days)
+outputs = model(
+    past_target=historical_data[..., 0].unsqueeze(-1),  # Close price is the target
+    past_observed_mask=torch.ones_like(historical_data[..., 0].unsqueeze(-1)),
+    past_feat_dynamic_real=historical_data[..., 1:], # Other dynamic features
+    static_categorical_features=static_features
+)
+
+# Output is a distribution object (e.g., Gaussian) or a tensor of shape [Batch, Prediction Length, Output Size]
+# For a point estimate:
+forecast = outputs.prediction
+print(f"Shape of 7-day forecast for 5 assets: {forecast.shape}") 
+# Expected output: torch.Size([5, 7, 1])