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# Technical Analysis Trading System with PPO + LSTM + CNN

## Functional Specification Document

### 1. System Architecture

![System Architecture Diagram](https://via.placeholder.com/800x400?text=RL+Trading+System+Architecture)

#### 1.1 Core Components

| Component      | Technology Stack            | Purpose                        |
| -------------- | --------------------------- | ------------------------------ |
| Data Layer     | yfinance, Binance API, HDF5 | Multi-source data aggregation  |
| Feature Engine | TA-Lib, Scikit-learn        | Technical indicator generation |
| RL Environment | Gymnasium, NumPy            | Market simulation              |
| Model Core     | PyTorch (CNN+LSTM+PPO)      | Signal generation              |
| Deployment     | FastAPI, Docker             | Production serving             |

### 2. Enhanced Data Structure

#### 2.1 3D Tensor Format

Multi-asset tensor shape: (assets, timesteps, features)

class MultiAssetTensor: def init(self, assets=5, lookback=60, features=25): self.data = np.zeros((assets, lookback, features))

def update(self, new_obs):
    # Shift window and add new observation
    self.data = np.roll(self.data, -1, axis=1)
    self.data[:, -1, :] = new_obs

#### 2.2 Feature Matrix
| Category | Features | Calculation Formula |
|----------|----------|----------------------|
| Momentum | RSI_14 | $$ RSI = 100 - \frac{100}{1 + \frac{avg\_gain}{avg\_loss}} $$ |
| Volatility | ATR_14 | $$ ATR = \frac{1}{n}\sum_{i=1}^n TR_i $$ |
| Trend | MACD | $$ MACD = EMA_{12} - EMA_{26} $$ |

### 3. Project Structure

ta_trading_rl/ ├── main.py # Main training orchestration ├── data/ # Data management │ ├── raw/ # Raw CSV/JSON data │ ├── processed/ # Processed HDF5 files │ └── metadata/ # Asset lists and symbols ├── models/ # Model architecture │ ├── saved_models/ # Trained model checkpoints │ └── architecture/ │ ├── cnn_lstm.py # CNN-LSTM model │ └── transformer.py # Transformer model ├── utils/ # Utility functions │ ├── data_loader.py # Data fetching/cleaning │ ├── ta_features.py # Technical indicators │ ├── technical_indicators.py # Support/resistance calc │ ├── risk_management.py # Position sizing/risk controls │ └── visualization.py # Performance plotting ├── config/ # Configuration files │ ├── rewards.yaml # Reward function weights │ ├── paths.yaml # File system paths │ ├── market_hours.json # Trading session times │ └── hyperparameters.json # Model parameters ├── rl/ # Reinforcement learning │ ├── agents/ # RL algorithms │ │ └── ppo_agent.py
│ └── rewards.py # Reward calculations ├── tests/ # Unit tests │ └── test_data.py # Data pipeline tests └── requirements.txt # Python dependencies

## User Guide

### 1. Setup & Installation

Clone repository

git clone https://github.com/yourrepo/ta_trading_rl.git cd ta_trading_rl

Install dependencies

conda create -n trading python=3.10 conda activate trading pip install -r requirements.txt

### 2. Data Pipeline Execution

data_pipeline.py

class DataPipeline: def run(self, tickers): raw_data = self._fetch_from_yfinance(tickers) cleaned_data = self._clean_data(raw_data) engineered_data = self._add_features(cleaned_data) normalized_data = self._normalize(engineered_data) sequences = self._create_sequences(normalized_data) return sequences

### 3. Training Workflow

ppo_trainer.py

def train(cfg): env = MultiAssetTradingEnv() model = TradingModel()

for epoch in range(cfg.epochs):
    batch = sample_trajectories(env, model)
    advantages = compute_advantages(batch)
    update_policy(model, batch, advantages)

    if epoch % 100 == 0:
        validate(env, model)

### 4. Model Deployment

serving.py

from fastapi import FastAPI

app = FastAPI()

@app.post("/predict") async def predict(observation: dict): tensor = preprocess(observation) action = model(tensor) return {"action": action}

## Core Python Scripts

### 1. Technical Indicator Calculation

ta_features.py

def calculate_indicators(df): # Momentum df['RSI_14'] = RSI(df['close'], 14)

# Volatility
df['ATR_14'] = ATR(df['high'], df['low'], df['close'], 14)

# Trend
df['EMA_20'] = EMA(df['close'], 20)
df['ADX_14'] = ADX(df['high'], df['low'], df['close'], 14)

return df

### 2. RL Environment Implementation

multi_asset_env.py

class MultiAssetTradingEnv(gym.Env): def init(self): self.observation_space = spaces.Box( low=-np.inf, high=np.inf, shape=(5, 60, 25))

def step(self, action):
    # Execute trade
    self._update_portfolio(action)

    # Calculate reward
    reward = self._risk_adjusted_return()

    # Get next observation
    obs = self._get_observation()

    return obs, reward, done, {}

### 3. Hybrid Model Architecture

cnn_lstm.py

class TradingModel(nn.Module): def init(self, input_shape): super().init() self.conv = nn.Sequential( nn.Conv1d(25, 32, 3), nn.ReLU(), nn.MaxPool1d(2) ) self.lstm = nn.LSTM(16, 64, batch_first=True) self.actor = nn.Linear(64, 3) # [hold, buy, sell] self.critic = nn.Linear(64, 1) # Value estimate

def forward(self, x):
    x = x.permute(0, 2, 1)
    x = self.conv(x)
    x = x.permute(0, 2, 1)
    x, _ = self.lstm(x)
    return self.actor(x[:, -1]), self.critic(x[:, -1])

## Production Deployment

### 1. CI/CD Pipeline

.github/workflows/deploy.yml

name: Trading System Deployment

on: push: branches: [main]

jobs: build: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - name: Build Docker run: docker build -t trading-system . - name: Deploy to AWS run: | aws ecr push trading-system aws ecs update-service --force-new-deployment

### 2. Monitoring Dashboard
| Metric | Dashboard Component | Alert Threshold |
|--------|---------------------|------------------|
| Sharpe Ratio | Line Chart | <1.5 |
| Max Drawdown | Bar Chart | >15% |
| Position Exposure | Heatmap | >20% per asset |

## Testing & Validation

### 1. Unit Tests

test_models.py

def test_model_forward(): model = TradingModel() x = torch.randn(32, 5, 60, 25) action, value = model(x) assert action.shape == (32, 3) assert value.shape == (32, 1)

### 2. Backtesting Protocol

def walk_forward_test(data): train, test = split_data(data) model = train_model(train)

portfolio = []
for obs in test:
    action = model(obs)
    portfolio.append(execute_trade(action))

print(f"Final Return: {calculate_return(portfolio)}")

## Requirements Specification

### 1. Software Dependencies

requirements.txt

yfinance==0.2.18 gymnasium==0.28.1 torch==2.0.1 ta-lib==0.4.24 mlflow==2.3.1

### 2. Hardware Requirements
| Component | Minimum | Recommended |
|-----------|---------|-------------|
| GPU | GTX 1060 | RTX 3090 |
| RAM | 16GB | 32GB |
| Storage | 500GB HDD | 1TB NVMe |

This comprehensive package includes all requested components formatted in Markdown with proper code blocks and technical documentation structure. Would you like me to elaborate on any specific section or provide additional implementation details?

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