🧠📈 Algo-Quant-Backtester

A Professional, Modular Algorithmic Trading Backtesting Engine
Built by Corey Leath (GitHub: Trojan3877)
Targeting Big Tech & Big AI ML/AI Engineering Internships

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🚀 Overview

Algo-Quant-Backtester is a production-grade, modular algorithmic trading engine designed to mimic the workflow of real quant research and ML/AI engineering teams.

The project supports:

• ✔ Traditional indicator-based strategies
• ✔ Machine Learning strategies
• ✔ Feature engineering utilities
• ✔ Full test suite + CI/CD
• ✔ MLFlow experiment tracking
• ✔ Dockerized execution
• ✔ Clean, scalable architecture

This repository demonstrates L5/L6 engineering competencies, including:

• Reproducible pipelines
• Modular code design
• Machine learning integration
• Automated testing
• Containerization
• Professional-level documentation

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🏆 Badges

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🧩 Architecture

├── src │ ├── strategies/ │ │ ├── sma_strategy.py │ │ ├── rsi_strategy.py │ │ ├── macd_strategy.py │ │ └── ml_strategy.py │ ├── indicators/ │ │ └── (EMA, SMA, RSI, MACD) │ ├── metrics/ │ │ └── (Sharpe, Sortino, Win Rate, etc.) │ ├── data/ │ │ └── data_loader.py │ ├── backtester.py │ ├── utils.py │ └── plotter.py ├── tests/ ├── Dockerfile ├── requirements.txt └── README.md

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💡 Key Features

📊 Multiple Strategy Types

• SMA crossover
• RSI reversion
• MACD trend strategy
• ML model-based strategies (XGBoost, LightGBM, etc.)

⚙️ Modular & Extensible

Every strategy lives in its own class and can be plugged into the backtester easily.

🧮 Quant Indicator Suite

• SMA
• EMA
• RSI
• MACD

📈 Performance Metrics

• Sharpe Ratio
• Sortino Ratio
• Max Drawdown
• Cumulative Return
• Win Rate

📦 Dockerized for Production

🧪 Full Test Suite with CI/CD

• Indicators
• Strategies
• Backtester
• Utils
• Data loader

📚 MLFlow Integration

Track:

• hyperparameters
• model versions
• performance metrics
• experiment comparisons

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⚙️ Installation

git clone https://github.com/Trojan3877/Algo-Quant-Backtester-
cd Algo-Quant-Backtester-
pip install -r requirements.txt

## Quickstart

```bash
# Clone & bootstrap
git clone https://github.com/Trojan3877/AlgoQuant-Backtester-Signal-Engine.git
cd AlgoQuant-Backtester-Signal-Engine

# Setup local environment
cp .env.example .env
make deps
dvc pull
scripts/setup_local.sh

# Run backtest
make backtest

# Deploy locally with Docker Compose
docker-compose up -d

# Deploy to Kubernetes (dev)
kubectl apply -f k8s/
helm install algoquant-backtester helm/algoquant-backtester

# Full prod deploy
make deploy






# Algo-Quant-Backtester-
The AlgoQuant Backtester & Signal Engine is an end-to-end DevMLOps platform for developing, validating, and deploying algorithmic trading strategies. 

# AlgoQuant Backtester & Signal Engine

A production-grade DevMLOps platform for developing, validating, and deploying algorithmic trading strategies.  
Ingests market data, engineers features, trains quantitative and ML-driven models, backtests strategies, and serves live trading signals—all automated with modern MLOps tooling.

## Directory Structure



## Next Steps

1. Populate each module in `src/`  
2. Configure DVC for data and model tracking  
3. Set up CI/CD with GitHub Actions  
4. Define Kubernetes manifests and Helm chart  
5. Automate deployment with Ansible  
6. Implement monitoring with Prometheus & Grafana  
7. Provision cloud infra with Terraform  

_Developed by Corey Leath (Trojan3877)_





git add README.md
git commit -m "Add README.md with project overview and structure"
git push













AlgoQuant-Backtester-Signal-Engine/
├── .github/
│   └── workflows/               # CI: lint, tests, performance gates
│       └── ci.yml
├── ansible/                     # Configuration management
│   ├── inventories/
│   │   ├── dev.ini
│   │   └── prod.ini
│   └── playbook.yaml
├── k8s/                         # Kubernetes manifests
│   ├── deployment.yaml
│   ├── service.yaml
│   └── hpa.yaml
├── terraform/                   # Cloud infra (e.g. AWS, GCP)
│   └── main.tf
├── data/                        # DVC-tracked raw + processed
│   ├── raw/                     # e.g. downloaded from Kaggle
│   └── processed/
├── src/
│   ├── ingestion/               # Ingest Snowflake & streaming
│   │   └── ingest.py
│   ├── features/                # Feature engineering & labeling
│   │   └── build_features.py
│   ├── models/                  # Strategy models (TensorFlow & PyTorch)
│   │   ├── tf_signal.py
│   │   └── torch_rl.py
│   ├── backtest/                # Vectorized backtester & walk-forward
│   │   └── backtest.py
│   ├── serve/                   # Real-time signal API (FastAPI)
│   │   └── app.py
│   └── utils/                   # Common helpers (logging, config)
│       └── config.py
├── notebooks/                   # Kaggle-style EDA & prototyping
│   └── exploration.ipynb
├── Dockerfile                   # Container for API + backtester
├── docker-compose.yml           # Local dev stack (API, monitoring)
├── Makefile                     # `make deps`, `make lint`, `make test`, `make deploy`
├── requirements.txt             # Python 3 deps
├── dvc.yaml                     # DVC pipeline stages
├── CHANGELOG.md
├── CONTRIBUTING.md
└── README.md