Coding an AI Trading Bot

As someone with a background in both finance and technology, I've always been intrigued by the intersection of these fields. The concept of creating an AI-powered trading bot fascinated me. After months of research, coding, and testing, I finally built one that could autonomously analyze market data, predict trends, and execute trades. Here’s how I did it, step by step.

Step 1: Building the Baseline Bot

The first step in creating my AI trading bot was to establish a solid foundation. I started by setting up a Python script to gather historical market data. Using the Lumibot library, I could create a trading framework that allowed me to backtest various strategies.

python

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import requests

import pandas as pd

from datetime import datetime, timedelta

API_KEY = 'your_api_key'

BASE_URL = 'https://financialdataapi.com/api/v1/'

def get_historical_data(symbol, start_date, end_date):

url = f"{BASE_URL}historical/{symbol}"

params = {

'start_date': start_date.strftime('%Y-%m-%d'),

'end_date': end_date.strftime('%Y-%m-%d'),

'api_key': API_KEY

}

response = requests.get(url, params=params)

data = response.json()

return pd.DataFrame(data)

start_date = datetime.now() - timedelta(days=365)

end_date = datetime.now()

symbol = 'AAPL'

historical_data = get_historical_data(symbol, start_date, end_date)

historical_data.to_csv('historical_data.csv', index=False)

This initial setup allowed me to collect and store historical data, which was crucial for developing and testing my trading strategies.

Step 2: Position Sizing and Limits

Once I had the data, the next step was to implement a method for dynamically sizing my trading positions. Proper position sizing is essential to managing risk and ensuring that no single trade can significantly impact my portfolio.

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def position_sizing(cash, last_price, risk_percentage=0.5):

cash_at_risk = cash * risk_percentage

quantity = cash_at_risk // last_price

return int(quantity)

cash = 10000

last_price = 150

quantity = position_sizing(cash, last_price)

print(f'Quantity to trade: {quantity}')

By calculating position sizes based on my available cash and a predefined risk percentage, I could ensure that my trades were appropriately scaled.

Step 3: Integrating News Sentiment Analysis

Market sentiment often drives price movements, so incorporating news sentiment analysis into my bot was a logical next step. I used a machine learning model to gauge market sentiment from recent news articles and adjust my trading strategies accordingly.

python

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import requests

from transformers import pipeline

def get_news_sentiment(symbol):

url = f"https://newsapi.org/v2/everything?q={symbol}&apiKey=your_news_api_key"

response = requests.get(url)

articles = response.json()['articles']

sentiment_model = pipeline("sentiment-analysis")

sentiments = [sentiment_model(article['title'])[0] for article in articles]

return sentiments

symbol = 'AAPL'

sentiments = get_news_sentiment(symbol)

print(sentiments)

This function gathered news articles about my target stock and used a pre-trained sentiment analysis model to determine whether the news was positive or negative.

Step 4: Building the Trading Strategy

With the data and sentiment analysis in place, I built the core trading strategy. This involved setting rules for when to buy and sell based on the data and sentiment scores.

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def trading_strategy(cash, last_price, sentiment_score, threshold=0.8):

if sentiment_score > threshold:

action = "BUY"

elif sentiment_score < -threshold:

action = "SELL"

else:

action = "HOLD"

quantity = position_sizing(cash, last_price)

return action, quantity

sentiment_score = 0.9 # Example sentiment score

action, quantity = trading_strategy(cash, last_price, sentiment_score)

print(f'Action: {action}, Quantity: {quantity}')

The strategy ensured that trades were only executed when the sentiment score indicated a strong market movement, thus minimizing unnecessary trades.

Step 5: Execution and Backtesting

Finally, I integrated the bot with a brokerage API to execute trades and conducted thorough backtesting to evaluate its performance. I chose Alpaca, a commission-free brokerage, for this purpose.

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import alpaca_trade_api as tradeapi

API_KEY = 'your_alpaca_api_key'

API_SECRET = 'your_alpaca_api_secret'

BASE_URL = 'https://paper-api.alpaca.markets'

api = tradeapi.REST(API_KEY, API_SECRET, BASE_URL, api_version='v2')

def execute_trade(action, symbol, qty):

if action == "BUY":

api.submit_order(

symbol=symbol,

qty=qty,

side='buy',

type='market',

time_in_force='gtc'

)

elif action == "SELL":

api.submit_order(

symbol=symbol,

qty=qty,

side='sell',

type='market',

time_in_force='gtc'

)

action, quantity = trading_strategy(cash, last_price, sentiment_score)

execute_trade(action, symbol, quantity)

By running backtests, I could fine-tune my bot’s parameters and strategy to optimize its performance. This process allowed me to identify and correct any weaknesses before deploying the bot in a live trading environment.

Conclusion

Creating an AI trading bot was an enlightening experience that combined my interests in finance and technology. Through methodical planning, coding, and testing, I built a bot capable of autonomously analyzing market data, predicting trends, and executing trades. While the journey was challenging, the knowledge and skills I gained were invaluable. For anyone looking to explore the world of algorithmic trading, the rewards of perseverance and continuous learning are immense.