💡 Why Trader++?

I built Trader++ because I wanted a tool that didn’t lie to me.

I needed something that could:

• Let me write strategies quickly
• Simulate realistically
• Go from backtest → paper → live with zero rewrites
• Show me how my portfolio’s actually doing, holistically!

Existing tools? Clunky. Proprietary. Not programmable enough.

So I built it — for myself first. Now, it’s for every quant who thinks like a developer.

Unleash the power of modular, realistic, and extensible portfolio simulation.

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✨ Why Trader++?

Trader++ isn’t just another backtesting tool. It’s a full-fledged quant trading engine built for:

• True Portfolio Simulation: Manage multiple assets, cash, and trades as real portfolios—not just isolated strategies.
• Plug-and-Play Modularity: Swap in new strategies, data sources, or risk guardrails with minimal code.
• Event-Driven Realism: Simulate trades, slippage, and portfolio changes in a way that mimics real markets.
• Powerful Guardrails: Risk management hooks that go beyond stop-losses—unregister assets, enforce capital limits, and more.
• Transparent & Hackable: Built for experimentation, learning, and research. Every core component is swappable and inspectable.

How is it different from Backtrader, Zipline, or QuantConnect?

• 🧩 Cleaner separation of concerns: Market data, strategies, execution, and portfolio logic are fully decoupled.
• 🛡️ Advanced guardrails: Custom risk modules, not just basic stop-losses.
• 💡 Portfolio as a first-class citizen: Track capital, trades, and metadata in one place.
• 🧪 Designed for research: Easy to debug, extend, and run controlled experiments.
• 🌱 Open, modern, and Pythonic: No black boxes, no vendor lock-in, and ready for your next big idea.

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🏆 MVP Roadmap

1. Unified Execution Engine

• Common interface: BacktestExecutor, PaperExecutor, LiveExecutor
• Live broker integration (Alpaca, IBKR, TD Ameritrade)
• Real-time slippage, partial fills, latency simulation

2. Advanced Strategy Framework

• YAML/DSL config loader for no-code strategies
• Multi-frequency, multi-asset support
• ML model integration (Torch/Sklearn) + MLFlow/W&B logging

3. Modular Risk & Portfolio Control

• Position sizing (Kelly, risk parity, volatility targeting)
• Real-time rule engine (e.g., freeze strategy on drawdown)
• Hierarchical/nested portfolios with capital/risk constraints

4. Performance & Attribution Analytics

• Alpha, beta, Sharpe, Sortino, Calmar
• Attribution by asset, sector, strategy
• Trade replay and audit trail

5. Interactive Visualization

• Streamlit/Dash hybrid dashboard
• Trade timeline, rolling metrics, slippage/turnover/holding histograms

6. Scalable Simulation Engine

• Multiprocessed/multithreaded backtesting core
• GPU acceleration for ML strategies
• Clean, event-driven simulation loop

7. Data Layer

• SQL/Parquet backend support
• Live feed adapters
• Flexible bar aggregators (time, volume, event)

8. AI & Data-Driven Research

• Sentiment and alt-data adapters (Reddit, news, Google Trends)
• Cointegration, Kalman filter, auto-correlation modules

9. Tests, Docs, Demos

• Unit tests for each module
• Example strategies (momentum, mean-reversion, breakout)
• Jupyter/Streamlit demo notebooks

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🚦 MVP Status (June 2025)

Feature	Status	Notes
Unified Execution Engine	✅ Complete	Backtest, Paper, Live modes implemented with shared API. Paper & Live mode requires broker API implementation.
Modular Strategy Framework	✅ Complete	StrategyBase and example strategies present. Plug-and-play.
Portfolio/Risk Management	✅ Complete	Portfolio class, guardrails, position sizing hooks implemented.
Analytics & Attribution	✅ Partial	Core metrics (Sharpe, alpha, etc.) present. Some advanced analytics in progress.
Interactive Dashboard	⚠️ Partial	Streamlit app exists, some features stubbed or in progress.
Scalable Simulation Engine	⚠️ Partial	Event-driven core present; multiprocessing support basic or planned.
Data Layer	✅ Complete	Data ingestion, caching, and basic adapters present.
ML/DSL Integration	🚧 Planned	ML model integration and YAML/DSL loader planned.

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Next Steps (Post-MVP)

• Expand broker integrations for live trading
• Enhance dashboard with more analytics and controls
• Add ML/DSL strategy support
• Improve test coverage and documentation

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🧠 Objective

Empower quants and developers to:

• Cleanly separate market data, strategies, execution logic, and portfolio tracking
• Run realistic, event-driven backtests and simulations
• Plug-and-play both single-asset and multi-asset strategies

Built for robust experimentation and real-world readiness, with proper portfolio management and capital accounting.

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🗺️ System Architecture

flowchart TD
    subgraph Data Layer
      A[MarketData]
    end
    subgraph Strategy Layer
      B[StrategyBase]
    end
    subgraph Execution Layer
      C[PortfolioExecutor]
      D[Guardrails]
    end
    subgraph Portfolio & Analytics
      E[Portfolio]
      F[Performance Analytics]
    end
    A -- Price/Volume Data --> B
    B -- Signals --> C
    C -- Orders/Trades --> E
    C -- Risk Checks --> D
    D -- Approve/Block Trades --> C
    E -- Holdings/PnL --> F
    F -- Reports --> E

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🏗️ Project Structure & Architecture

• contracts/ — Core contracts and abstract base classes (Portfolio, StrategyBase, Executor)
• core/ — Core logic, execution engines, simulation loop
• strategies/ — Example and user strategies (momentum, buy & hold, etc.)
• data_ingestion/, data_cache/ — Data loaders, adapters, and caching for reproducible research
• analytics/, ml_engine/ — Analytics, reporting, and ML integrations
• dashboard/ — Streamlit/Dash dashboard for visualization
• run_backtest.py — CLI entry point to run backtests
• main.py, run/ — Additional CLI tools and runners

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🚦 Development Roadmap (Next Steps)

1. Finalize Core Contracts
   • Audit and refine Portfolio, StrategyBase, and PortfolioExecutor for strict modularity and safety (no future leaks).
2. Strategy API
   • Enforce and document the generate_signals interface. Add more example strategies.
3. Backtesting Engine
   • Expand test coverage and logging in run_backtest.py and core/executors/backtest.py.
4. Data Layer
   • Ensure robust, reproducible data ingestion and caching. Document data contracts.
5. CLI & Developer Experience
   • Improve CLI usability and add clear usage examples.
6. Dashboard & Analytics
   • Expand analytics and dashboard integration for portfolio and strategy reporting.
7. Documentation
   • Add docstrings, inline docs, and contribution guidelines for new modules and strategies.

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🔧 Core Components

Module	Purpose
Portfolio	Tracks assets, cash, trades, and strategy metadata. Self-contained unit.
PortfolioExecutor	Orchestrates strategy execution, manages trade logic, evaluates guards.
MarketData	Historical price data & sliding window views for strategies.
StrategyBase	Interface for strategy design, single/multi-asset support.
Backtester	Runs simulations, exports performance reports and logs.

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💡 Main Features

• 📈 Backtesting Engine — Realistic execution, guardrails, cash shares checks
• 🧠 Pluggable Strategy Interface — Stateful/stateless signal generation
• 💼 Portfolio Tracking — Accurate PnL with trade logs, equity curves
• 🛡️ GuardrailBase System — Risk management hooks (stop-loss, asset unregister)
• 📊 Performance Reporting — Sharpe, max drawdown, win rate, CAGR, more
• 🔬 Benchmark Comparison — Alpha, beta, vs SPY or other tickers
• 🧪 Test Strategies — Debug pipeline (e.g., “buy once on day 1”)

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🚀 Quickstart

1. Install Requirements

   pip install -r requirements.txt

2. Run a Backtest

   python main.py --start=2022-05-29 --end=2025-05-29 --cash 50000 --plot --export --tickers=META --refresh --strategy=momentum --benchmark=META --guardrail=trailing_stop_loss

3. Add a New Strategy
   • Implement a new class in strategies/ inheriting from StrategyBase and implementing generate_signals().
   • Register your strategy by importing it in strategies/__init__.py.

4 Add a New Guardrail

• Implement a new class in guardrails/ inheriting from GuardrailBase and implementing evaluate().
• Register your guardrail by importing it in guardrails/__init__.py.

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🤝 Contributing

• Add new strategies, data adapters, or analytics modules as composable units.
• Follow modular design and document your code.
• PRs and issues welcome!

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🌱 Vision for Future Work

See the MVP Roadmap above for our ambitious next steps!

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📄 License

Distributed under the Apache-2.0 License.

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📬 Contact

Open an issue or reach out at [rohitashwachaks@gmail.com] for questions and collaboration!

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Enjoy building and experimenting with Trader++! 🚀