# Directory structure for based-trading-agent.zip:

requirements.txt:
numpy>=1.21.0
pandas>=1.3.0
python-dateutil>=2.8.2
typing>=3.7.4
pytest>=6.2.5
logging>=0.5.1.2

README.md:
# 🚀 Based Trading Agent

The most based trading bot implementation for degens who want to automate their gains! Built different - with AI that makes your average trading bot look like a calculator from 2009.

## 🔥 Features

- Position sizing more protective than your hardware wallet
- Risk management that keeps you from getting rekt
- Technical analysis smoother than a MEV bot
- Performance tracking like a block explorer
- Sentiment analysis that reads market vibes better than CT

## 🛠️ Installation

```bash
git clone https://github.com/your-repo/based-trading-agent
cd based-trading-agent
pip install -r requirements.txt
```

## 🏃‍♂️ Quick Start

```python
from based_trading.agent import BasedTradingAgent

# Initialize your chad trading agent
agent = BasedTradingAgent(
    max_position_size=1.0,    # How much to ape in
    risk_per_trade=0.02,      # Stay safu with 2% risk
    min_risk_ratio=1.5,       # Like a pro trader
    leverage=1.0,             # Set to 100x to get rekt
    ape_mode=False           # Enable for maximum degen
)

# Start trading like a boss
result = agent.execute_trade(your_market_data)
```

## 🧪 Testing

```bash
pytest tests/
```

## 📜 License

WAGMI License v1.0 - Do whatever you want, just don't get rekt!

based_trading/
  __init__.py:
"""
Based Trading Agent - The ultimate WAGMI trading solution
"""

from .agent import BasedTradingAgent
from .position import Position

__version__ = '1.0.0'

based_trading/agent.py:
[Previous agent code goes here, unchanged]

based_trading/position.py:
from dataclasses import dataclass
from datetime import datetime

@dataclass
class Position:
    """
    Track our bags like a true degen 💎🙌
    """
    entry_price: float
    size: float
    side: str  # 'long' or 'short'
    entry_time: datetime
    take_profit: float
    stop_loss: float

tests/test_agent.py:
import pytest
from datetime import datetime
import pandas as pd
from based_trading import BasedTradingAgent

def test_agent_initialization():
    """Test if agent initializes like a chad"""
    agent = BasedTradingAgent()
    assert agent.current_position is None
    assert agent.pnl_history == []
    assert agent.ape_mode is False

def test_position_sizing():
    """Test if position sizing stays safu"""
    agent = BasedTradingAgent(max_position_size=1.0, risk_per_trade=0.02)
    size = agent.calculate_position_size(price=100, stop_loss=95)
    assert size <= agent.max_position_size

def test_sentiment_analysis():
    """Test if sentiment analysis is more accurate than CT"""
    agent = BasedTradingAgent()
    data = pd.DataFrame({
        'close': [100, 101, 102, 103, 102, 101, 100],
        'volume': [1000, 1100, 1200, 1300, 1200, 1100, 1000]
    })
    sentiment = agent.analyze_market_sentiment(data)
    assert -1 <= sentiment <= 1

examples/basic_usage.py:
from based_trading import BasedTradingAgent
import pandas as pd

# Create some example market data
data = pd.DataFrame({
    'close': [100, 101, 102, 103, 102, 101, 100],
    'volume': [1000, 1100, 1200, 1300, 1200, 1100, 1000]
})

# Initialize the most based trading agent
agent = BasedTradingAgent(
    max_position_size=1.0,
    risk_per_trade=0.02,
    min_risk_ratio=1.5,
    leverage=1.0,
    ape_mode=False  # Set to True to go full degen
)

# Execute some galaxy brain trades
for _ in range(5):
    result = agent.execute_trade(data)
    print(f"Trade result: {result}")

# Check them gains
stats = agent.get_portfolio_stats()
print(f"Portfolio stats: {stats}")