https://github.com/ksm26/artificial-intelligence-in-quantitative-finance
End-to-end deep learning pipeline for quantitative finance, using LSTM and Transformer models to forecast returns and power dynamic, optimized portfolios. From data collection to backtesting, it delivers reproducible results, statistical validation, and clear visual insights for smarter financial strategies.
https://github.com/ksm26/artificial-intelligence-in-quantitative-finance
algorithmic-trading backtesting datascience financeai financial-markets financial-modeling fintech investment-strategies lstm portfolio-management predictive-analytics python pytorch quantitative-finance stock-market timeseries-forecasting transformers
Last synced: 2 months ago
JSON representation
End-to-end deep learning pipeline for quantitative finance, using LSTM and Transformer models to forecast returns and power dynamic, optimized portfolios. From data collection to backtesting, it delivers reproducible results, statistical validation, and clear visual insights for smarter financial strategies.
- Host: GitHub
- URL: https://github.com/ksm26/artificial-intelligence-in-quantitative-finance
- Owner: ksm26
- Created: 2025-08-04T18:27:49.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-08-10T11:22:56.000Z (2 months ago)
- Last Synced: 2025-08-10T12:29:44.348Z (2 months ago)
- Topics: algorithmic-trading, backtesting, datascience, financeai, financial-markets, financial-modeling, fintech, investment-strategies, lstm, portfolio-management, predictive-analytics, python, pytorch, quantitative-finance, stock-market, timeseries-forecasting, transformers
- Language: Jupyter Notebook
- Homepage: https://github.com/ksm26/Artificial-Intelligence-in-Quantitative-Finance
- Size: 3.39 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 📈 AI in Quantitative Finance — Deep Learning for Portfolio Management
This project implements the end-to-end pipeline described in
**"Artificial Intelligence in Quantitative Finance: Leveraging Deep Learning for Smarter Portfolio Management and Asset Allocation"**.
We combine **LSTM** and **Transformer** models for **financial return forecasting**, integrate the predictions into a **dynamic portfolio allocation strategy**, and evaluate using **comprehensive backtesting and statistical testing**.
---
## 📂 Project Structure
project-root/\
│
├── data/\
│ ├── raw/ # Raw downloaded price data\
│ ├── processed/ # Preprocessed features & targets\
│ │ ├── features_scaled.csv\
│ │ ├── targets.csv\
│ │ ├── scaler_features.pkl\
│ │ └── index_splits.json\
│
├── notebooks/\
│ ├── 01_data_exploration.ipynb\
│ ├── 02_model_training.ipynb\
│ ├── 03_backtest_and_eval.ipynb\
│
├── results/\
│ ├── checkpoints/ # Saved LSTM & Transformer models by timestamp\
│ │ ├── lstm/\
│ │ └── transformer/\
│ ├── figures/ # Plots & visualizations\
│ ├── metrics_lstm.csv\
│ ├── metrics_transformer.csv\
│ ├── predictions_lstm.csv\
│ └── predictions_transformer.csv\
│
├── src/\
│ ├── data_fetch.py # Step 1: Download financial data\
│ ├── preprocessing.py # Step 2: Build features, scale, save datasets\
│ ├── train.py # Step 3: Train LSTM or Transformer models\
│ ├── predict.py # Step 5: Generate rolling forecasts\
│ ├── optimizer.py # Portfolio optimization logic\
│ ├── backtest.py # Step 5: Backtesting engine\
│ ├── metrics.py # Step 6: Performance metrics & statistical tests\
│ ├── evaluate_and_plot.py # Step 6: Run evaluation, save metrics & plots\
│ └── datasets.py # SequenceDataset loader\
│
└── environment.yml # Conda environment with dependencies\
---
## ⚙️ Pipeline Overview
The workflow follows **six main steps**:
### **Step 1 — Data Collection**
- **Script:** `src/data_fetch.py`
- Fetches historical financial price data from the internet (e.g., Yahoo Finance).
- Saves raw prices to `data/raw/`.
**Run:**
```bash
python src/data_fetch.py --start 2010-01-01 --end 2023-12-31
```
### **Step 2 — Data Preprocessing**
- **Script:** `src/preprocessing.py`
- Builds predictive features (technical indicators, returns, rolling stats).
- Scales features (StandardScaler) using train split only.
- Saves: `features_scaled.csv`, `targets.csv` , `scaler_features.pkl` , `index_splits.json`
**Run:**
```bash
python src/preprocessing.py --data data/processed/prices_usd.csv
```
### **Step 3 — Model Training**
- **Script:** `src/train.py`
- Defines LSTM and Transformer architectures.
- Saves checkpoints to `results/checkpoints///epoch_.pth`
**Run:**
```bash
# LSTM
python src/train.py --model lstm --epochs 50
# Transformer
python src/train.py --model transformer --epochs 50
```
### **Step 4 — Forecast Generation**
- **Script:** `src/predict.py`
- Loads latest checkpoint for the chosen model.
- Generates rolling walk-forward predictions for the test set.
- Saves to `results/predictions_.csv`
**Run:**
```bash
python src/predict.py --model-type lstm
python src/predict.py --model-type transformer
```
### **Step 5 — Portfolio Optimization & Backtest**
- **Script:** `src/optimizer.py` → Mean-Variance Optimizer.
- `src/backtest.py` → Computes portfolio returns from weights & realized returns.
### **Step 6 — Evaluation & Plots**
- **Script:** `src/evaluate_and_plot.py`
- Loads predictions & realized returns.
- Computes:
- Annualized return, volatility, Sharpe ratio, Sortino ratio, Calmar ratio.
- RMSE & directional accuracy for forecasts.
- Statistical tests: Paired t-test, Jobson-Korkie-Memmel.
- Saves:
- Metrics CSV → `results/metrics_.csv`
- Plots → `results/figures/`
**Run:**
```bash
python src/evaluate_and_plot.py --model-type lstm
python src/evaluate_and_plot.py --model-type transformer
```
## 📊 Outcomes
- **LSTM & Transformer** deep learning models for return forecasting.
- Fully automated **data-to-decisions pipeline:**
Data fetching → Feature engineering → Model training → Prediction → Portfolio construction → Evaluation.
- **Dynamic portfolio allocation** using forecasts.
- **Comprehensive evaluation** with statistical significance testing.
- **Visual outputs** for cumulative returns, rolling Sharpe ratio, and portfolio weights.
## 🚀 What This Project Achieves
- Demonstrates **state-of-the-art AI techniques** (LSTM, Transformer) in finance.
- Provides a **research-ready backtesting framework**.
- Bridges **ML predictions** with portfolio management.
- Produces **replicable & extensible** results for further experimentation.
## 💻 Environment Setup
```bash
conda env create -f environment.yml
conda activate project
```
## 🏁 Quickstart
```bash
# 1. Fetch data
python src/data_fetch.py --start 2010-01-01 --end 2023-12-31
# 2. Preprocess
python src/preprocessing.py
# 3. Train model
python src/train.py --model lstm --epochs 50
# 4. Predict
python src/predict.py --model-type lstm
# 5. Evaluate
python src/evaluate_and_plot.py --model-type lstm
```