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‎README.md‎

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> **Unleash the power of modular, realistic, and extensible portfolio simulation.**
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---
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---
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## 🏗️ Project Structure & Architecture
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- `contracts/` — Core contracts and abstract base classes (Portfolio, StrategyBase, Executor)
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- `core/` — Core logic, execution engines, simulation loop
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- `strategies/` — Example and user strategies (momentum, buy & hold, etc.)
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- `data_ingestion/`, `data_cache/` — Data loaders, adapters, and caching for reproducible research
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- `analytics/`, `ml_engine/` — Analytics, reporting, and ML integrations
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- `dashboard/` — Streamlit/Dash dashboard for visualization
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- `run_backtest.py` — CLI entry point to run backtests
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- `main.py`, `run/` — Additional CLI tools and runners
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---
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## 🚦 Development Roadmap (Next Steps)
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1. **Finalize Core Contracts**
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- Audit and refine `Portfolio`, `StrategyBase`, and `PortfolioExecutor` for strict modularity and safety (no future leaks).
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2. **Strategy API**
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- Enforce and document the `generate_signals` interface. Add more example strategies.
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3. **Backtesting Engine**
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- Expand test coverage and logging in `run_backtest.py` and `core/executors/backtest.py`.
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4. **Data Layer**
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- Ensure robust, reproducible data ingestion and caching. Document data contracts.
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5. **CLI & Developer Experience**
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- Improve CLI usability and add clear usage examples.
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6. **Dashboard & Analytics**
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- Expand analytics and dashboard integration for portfolio and strategy reporting.
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7. **Documentation**
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- Add docstrings, inline docs, and contribution guidelines for new modules and strategies.
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---
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## 🔧 Core Components
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| Module | Purpose |
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---
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## 🚀 Quick Start
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```sh
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# Install dependencies
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pip install -r requirements.txt
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## 🚀 Quickstart
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# Run a backtest
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python run_backtest.py --strategy momentum --tickers AAPL,MSFT --start 2023-01-01 --end 2023-12-31 --plot
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# Or use the Streamlit UI
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streamlit run streamlit_app.py
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```
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1. **Install Requirements**
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```bash
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pip install -r requirements.txt
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```
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2. **Run a Backtest**
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```bash
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python run_backtest.py --strategy strategies/stock/momentum.py --portfolio configs/sample_portfolio.yaml
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```
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3. **Add a New Strategy**
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- Implement a new class in `strategies/` inheriting from `StrategyBase` and implementing `generate_signals()`.
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- Register your strategy in your backtest config or CLI.
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---
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## 🧩 Project Structure
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```text
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📦traderplusplus
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├── contracts
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│ ├── asset.py # Asset & CashAsset classes
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│ └── portfolio.py # Portfolio definition
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├── core
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│ ├── backtester.py # Runs simulation
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│ ├── executor.py # Executes trades
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│ ├── market_data.py # Loads, stores & queries market data
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│ ├── data_loader.py # Yahoo/Polygon loaders + caching
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│ ├── guardrails # Risk guardrail classes
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│ └── visualizer.py # Matplotlib + Plotly charts
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├── strategies
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│ ├── base.py # StrategyBase + factory
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│ └── stock
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│ ├── momentum.py # Example strategy
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├── analytics
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│ └── performance.py # Sharpe, Alpha etc.
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├── run_backtest.py # CLI tool
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└── streamlit_app.py # UI
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```
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## 🤝 Contributing
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- See the Development Roadmap above for high-priority areas.
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- Add new strategies, data adapters, or analytics modules as composable units.
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- Follow modular design and document your code.
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- PRs and issues welcome!
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## 🙌 Contributing
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Pull requests and suggestions are welcome! For major changes, please open an issue first to discuss what you’d like to change.
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---
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## 📄 License
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Distributed under the MIT License.

‎contracts/asset.py‎

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Original file line numberDiff line numberDiff line change
@@ -7,6 +7,10 @@ def __init__(self, ticker: str, shares: int = 0):
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self.shares = shares
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self.trade_history = []
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@property
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def balance(self):
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return self.shares
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def buy(self, quantity: int):
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self.shares += quantity
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‎core/backtester.py‎

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@@ -37,15 +37,27 @@ def run(self, tickers: List[str], start_date: str, end_date: str):
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common_index = common_index.sort_values()
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for current_date in common_index:
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# --- PURE STRATEGY: ONLY GENERATE SIGNALS ---
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signals = self.strategy.generate_signals(self.market_data, current_date=current_date)
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signals = self.strategy.generate_signals(self.market_data, current_date=current_date,
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positions=self.portfolio.positions)
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# --- EXECUTOR: SUBMIT ORDERS BASED ON SIGNALS ---
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for symbol, signal in signals.items():
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if signal == 1:
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order = Order(symbol=symbol, side=OrderSide.BUY, quantity=1, order_type=OrderType.MARKET)
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self.executor.submit_order(order)
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elif signal == -1:
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order = Order(symbol=symbol, side=OrderSide.SELL, quantity=1, order_type=OrderType.MARKET)
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for symbol, order_size in signals.items():
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if order_size == 0:
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continue
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order_side = OrderSide.BUY if order_size > 0 else OrderSide.SELL
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if order_side is not None:
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order = Order(
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symbol=symbol,
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side=order_side,
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quantity=order_size,
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order_type=OrderType.MARKET
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)
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self.executor.submit_order(order)
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# if signal == 1:
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# order = Order(symbol=symbol, side=OrderSide.BUY, quantity=1, order_type=OrderType.MARKET)
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# self.executor.submit_order(order)
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# elif signal == -1:
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# order = Order(symbol=symbol, side=OrderSide.SELL, quantity=1, order_type=OrderType.MARKET)
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# self.executor.submit_order(order)
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# --- EXECUTOR: ADVANCE TO NEXT STEP ---
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self.executor.step(current_date)
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‎core/executors/backtest.py‎

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message='Guardrail blocked order')
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continue
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try:
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if order.side.name == 'BUY':
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self.portfolio.execute_trade(current_time, order.symbol, 'BUY', fill_qty, avg_fill_price,
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note='Backtest Fill')
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else:
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self.portfolio.execute_trade(current_time, order.symbol, 'SELL', fill_qty, avg_fill_price,
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note='Backtest Fill')
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self.portfolio.execute_trade(current_time, order.symbol, order.side.name, fill_qty, avg_fill_price,
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note='Backtest Fill')
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# if order.side.name == 'BUY':
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# self.portfolio.execute_trade(current_time, order.symbol, 'BUY', fill_qty, avg_fill_price,
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# note='Backtest Fill')
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# else:
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# self.portfolio.execute_trade(current_time, order.symbol, 'SELL', fill_qty, avg_fill_price,
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# note='Backtest Fill')
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self.order_status[order_id] = OrderStatus.FILLED
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self.fills[order_id] = OrderResult(order_id=order_id, status=OrderStatus.FILLED,
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filled_quantity=fill_qty, avg_fill_price=avg_fill_price)
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if price is not None:
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net_worth += shares * price
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if net_worth != 50000:
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print('wth')
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self.equity_curve.append({'date': current_time, 'net_worth': net_worth})
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def get_equity_curve(self):

‎core/market_data.py‎

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def get_price(self, ticker: str, date: pd.Timestamp, price_type='Close') -> float | None:
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try:
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if ticker == 'CASH':
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return 1.0
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return self.data[ticker].loc[date, price_type]
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except KeyError:
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return None

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