How To Get Started With Algorithmic Trading In Python

Algorithmic trading allows you to make data-driven investment decisions leveraging the latest advancements in quantitative finance and machine learning. This comprehensive 2600+ word guide aims to provide you both a solid foundation to get started as well as advanced techniques to take your algorithms to the next level.

Choosing Python for Algorithmic Trading

Before jumping into the code, let‘s discuss why Python has become such a popular choice for algorithmic trading:

Flexibility to connect Python to Excel, databases, web platforms and more
Package ecosystem providing machine learning, data analysis and statistics functions out of the box
Ability to scale complex strategies with Python‘s simple syntax and readability
Rapid strategy iteration through scripts and notebooks like Jupyter
Community support through forums like StackOverflow at every experience level

These Python benefits have seen it adopted at top quant hedge funds as well as by solo independent traders. Whether you‘re looking to automate your own small account or build an institutional-grade infrastructure, Python delivers.

Next let‘s set up a proper Python environment for finance.

Setting Up Your Python Environment

Since Python is an interpreted language, it‘s important to control package versions to prevent breaking changes.

The standard approach is using virtual environments and requirements files:

pip install virtualenv
virtualenv tradingenv
source tradingenv/bin/activate

pip install pandas==1.1.1
pip freeze > requirements.txt

Now your trading algorithms will have reliable dependencies regardless which machine they run on.

Some other best practices are:

Structuring code into modules and packages
Version control with Git to track changes
IDEs like PyCharm for development conveniences like debugging and code completion

Spending time on these foundation steps will pay dividends when you‘re ready to scale out your strategy.

Importing and Manipulating Financial Data

Now we‘re ready to import some price data to analyze. A sample workflow would be:

Get raw CSV data
Load into Pandas dataframe
Resample to desired frequency
Calculate technical indicators
Visualize signals

Let‘s walk through a demonstration of this process. We‘ll use the Yahoo Finance API to get daily prices for Tesla stock:

import yfinance as yf
import pandas as pd
from datetime import datetime

tsla_data = yf.download(‘TSLA‘, 
                    start=datetime(2020,1,1), 
                    end=datetime(2020,12,31),
                    progress=False)

tsla_df = pd.DataFrame(tsla_data)

Now we can start manipulating this DataFrame however we want – let‘s visualize the closing price:

import matplotlib.pyplot as plt

tsla_df[‘Close‘].plot()

plt.title(‘Tesla Close Price 2020‘)
plt.xlabel(‘Date‘)
plt.ylabel(‘Close Price USD ($)‘)

plt.show()

From here we can calculate indicators like Bollinger Bands to identify opportunities:

# Calculate 20-day moving avg and std deviation
tsla_df[‘mavg‘] = tsla_df[‘Close‘].rolling(20).mean()
tsla_df[‘std‘] = tsla_df[‘Close‘].rolling(20).std()

# Upper and lower bands are 2 std deviations from mean
tsla_df[‘upper_band‘] = tsla_df[‘mavg‘] + (tsla_df[‘std‘] * 2)
tsla_df[‘lower_band‘] = tsla_df[‘mavg‘] - (tsla_df[‘std‘] * 2)

tsla_df[[‘Close‘, ‘upper_band‘, ‘lower_band‘]].plot()

We now have a solid workflow to import, resample, analyze, and visualize market data for strategy development.

Developing a Trading Strategy

Many beginners start by coding simple strategies like moving average crossovers. But today‘s institutional algorithms utilize much more advanced techniques:

Statistical arbitrage – Identify cointegrated securities for pair trading
Machine learning models – Neural networks or random forests for nonlinear relationships
Bayesian statistics – Continuously update probabilility price forecasts
Natural language processing – Extract signals from news and social media sentiment

Let‘s walk through a basic implementation of statistical arbitrage which is a staple of the top quantitative hedge funds.

The premise is that by finding highly correlated securities that temporarily diverge, we can trade the spread assuming it will eventually converge.

A classic example is trading S&P 500 ETFs like SPY and IVV which should theoretically track almost perfectly, but diverge in practice due to structural effects.

First we‘ll import price data for both ETFs:

import yfinance as yf
spy_data = yf.download(‘SPY‘, start=‘2020-01-01‘) 
ivv_data = yf.download(‘IVV‘, start=‘2020-01-01‘)

spy_df = pd.DataFrame(spy_data)
ivv_df = pd.DataFrame(ivv_data)

Next we combine them into a single dataframe, calculate the difference in closing prices, and plot the spread:

combined = pd.merge(spy_df[[‘Close‘]], ivv_df[[‘Close‘]], left_index=True, right_index=True, suffixes=[‘_spy‘,‘_ivv‘])

combined[‘price_diff‘] = combined[‘Close_spy‘] - combined[‘Close_ivv‘]  

combined[‘price_diff‘].plot()

Based on the price divergence, we can trade the ETF pair taking long/short positions when the spread widens beyond a threshold.

The full strategy would track an open position and incorporate realistic assumptions around slippage, commissions, and margin rates.

Backtesting for Strategy Evaluation

Any strategy looks good on limited sample data – the key is robust out-of-sample testing. Python has powerful backtesting frameworks to rerun your strategy over decades of historical data.

One popular library is Zipline which has pipelines for ingesting and analyzing financial data at scale:

from zipline.api import order, symbol
import zipline as zl

def initialize(context):
    context.spy_ivv = zl.asset.EquityPair(‘SPY‘, ‘IVV‘)

def handle_data(context, data):
  spread = data.current(context.spy_ivv, ‘price_diff‘) 

  if spread > 0.10:
    order(symbol(‘IVV‘), 100)
    order(symbol(‘SPY‘), -100) 

  if spread < -0.10:
    order(symbol(‘SPY‘), 100)
    order(symbol(‘IVV‘), -100)   

output = zl.run_algorithm(start=pd.Timestamp(‘2020-01-01‘, tz=‘utc‘), 
                          end=pd.Timestamp(‘2020-12-31‘, tz=‘utc‘),
                          initialize=initialize,
                          handle_data=handle_data)

Now we can analyze performance metrics like risk-adjusted return to compare strategies:

annual_return = output[‘portfolio_value‘][-1] / output[‘portfolio_value‘][0] ** (1/6) - 1
annual_volatility = output[‘portfolio_value‘].pct_change().std() * np.sqrt(252)

print(f‘Annual Return: {annual_return:.2%}‘)
print(f‘Annual Volatility: {annual_volatility:.2%}‘)  
print(f‘Sharpe Ratio: {(annual_return/annual_volatility):.2f}‘)

Typical ratios for decent strategies are 0.7+ Sharpe and 15-25% annual returns at 10-15% volatility. The art is balancing returns versus drawdowns through optimizing parameters like trade thresholds.

Automating Live Trading

Once you‘ve developed a profitable strategy through rigorous backtesting, the next step is live trading integration. Python connects well to brokerage APIs like Tradier and Alpaca.

Below we‘ll use the Alpaca SDK to check our account balance and place a test order:

import alpaca_trade_api as tradeapi

alpaca_api_key = ‘<your_api_key_here>‘

alpaca = tradeapi.REST(alpaca_api_key, ‘https://paper-api.alpaca.markets‘)  

account = alpaca.get_account()
print(account.status)

alpaca.submit_order(symbol=‘AAPL‘,
                    side=‘buy‘,
                    type=‘market‘,
                    qty=‘1‘)

I recommend starting with paper trading to confirm live performance matches your backtests before committing real capital.

Some best practices around live trading systems are:

Dedicated servers for uninterrupted market access
Real-time monitoring to catch issues immediately
Contingency plans for disasters or platform outages
Encryption, authentication, firewalls and more for security

Key Takeaways and Next Steps

The most successful traders continually refine and expand their knowledge. To recap, mastering the following skills will put you firmly on the path to excelling at algorithmic trading with Python:

Python fundamentals – variables, data structures, OOP concepts
Financial data analysis – import, clean, resample, visualize
Statistics and machine learning – regression, PCA, neural networks
Backtesting frameworks – optimize strategy performance
Automated trading via broker APIs – manage overall portfolio

This guide should provide a comprehensive introduction and starting point for algorithmic trading. Where to go from here:

Continue learning through my Python for Trading course with hands-on assignments
Paper trade strategies in live markets before risking capital
Join quant finance communities to stay on top of new research
Consider specializing in niche areas like crypto or derivatives trading

I hope this end-to-end walkthrough gave you both the big picture and specific techniques to start developing your own algorithms. Please reach out if you have any other questions – I‘m always happy to help fellow quants and traders on their journey!