Python for Finance – Algorithmic Trading Tutorial for Beginners

Algorithmic trading, which relies on computers to execute trades based on predefined strategies, has become an integral part of the financial markets…

Intermediate Trading Strategy Design

While the basic momentum strategy we implemented works reasonably well, real-world quant systems require more sophisticated logic along multiple dimensions:

Incorporating Advanced Technical Indicators

Instead of just simple moving averages, we can enhance our systems by adding indicators that capture volatility, trends, momentum etc.

Bollinger Bands add bands around a moving average based on standard deviation. This helps identify periods of high and low volatility:

upper_bb = SMA20 + 2 * Stock.rolling(20).std() 
lower_bb = SMA20 - 2 * Stock.rolling(20).std()

signal = Stock.Price < lower_bb  # Over sold

The Relative Strength Index (RSI) measures recent price performance as a ratio between 0-100. Values above 70 indicate overbought conditions useful for creating mean reversion signals:

rsi = talib.RSI(Stock.Price, 14)

signal = rsi > 70 # Stock over bought

Ensemble Models

We can improve accuracy by combining signals from multiple independent models into an ensemble strategy:

# Momentum signal
momentum_signal = Stock.return(20) > 0   

# Mean reversion signal
mr_signal = Stock.RSI(14) > 70

# Combined signal
signal = momentum_signal & mr_signal

Such a composite model utilizes more information for robustness.

Multiple Timeframes

Pro Traders also operate strategies across different timeframes – long term, swing, intraday – to better structure the complete trade lifecycle:

# Daily strategy   
daily_signal = create_high_level_signal()

# Hourly strategy  
hourly_signal = create_short_term_signal()  

# Execute intraday if daily gives directional bias
if daily_signal > 0:
   execute_hourly_strategy()

This enables strategies to operate in alignment across granularities.

Multi-Asset Trading

Expanding into multiple asset classes like equities, futures, forex etc also allows more diversification and opportunity:

# Stocks strategy
eq_signal = create_stock_signal()   

# Bitcoin strategy  
btc_signal = create_crypto_signal()   

ExecuteStrategy(eq_signal + btc_signal)

Having a portfolio of strategies across assets improves returns while lowering portfolio volatility.

As we can see, Python makes iterating on more advanced strategy logic very convenient. Next, let‘s understand how to better predict future price dynamics.

Additional Data Science and ML Methods

The Simple moving average example relied solely on past price data. We can enhance predictive capability using more advanced data science techniques:

Feature Engineering

Domain specific indicators around momentum, volatility etc can serve as smart inputs to ML models:

# Engineer features
df["Volume_Change"] = df["Volume"].pct_change(5)   
df["Price_ROC"] = df["Close"].diff(20) 

# Train model
model.fit(X=df[features], y=df["Return"])

Cross Validation

We should validate models out-of-sample rather than just fitting on all data:

from sklearn.model_selection import KFold

# Split data
kf = KFold(n_splits=10, shuffle=True)

for train, test in kf.split(X, y):
   model.fit(X[train], y[train]) 
   predict(X[test])

This simulates real-time deployment more closely.

Hyperparameter Tuning

We can automate finding the best model configurations rather than manual guessing:

from sklearn.model_selection import GridSearchCV

params = {"lr":[0.01,0.1],"max_depth":[3,5]}

gs = GridSearchCV(model, params, cv=5)
gs.fit(X,y)  

print(gs.best_params_)

Grid search finds optimal hyperparameters efficiently.

Neural Networks

Deep neural networks can uncover nonlinear relationships missed by classical linear/tree based models:

from tensorflow import keras

model = keras.Sequential()
model.add(Dense(32, input_shape=(X.shape[1],)))
model.add(keras.layers.Activation(‘relu‘))

model.compile(optimizer=‘adam‘, loss=‘mse‘)

Keras provides a very convenient API for constructing/training neural networks which can drive significantly better performance.

The great part about Python is how simple it makes applying these advanced analytical approaches. Let‘s now look at optimizing the next step – order execution.

Trade Execution and Order Management

The entry price at which we execute trades has significant P&L implications. Smart order placement algorithms can help reduce impact costs:

Execution Algorithms

Instead of naive market orders, we can slice large orders over time using sophisticated approaches:

Volume Weighted Average Price (VWAP) aims to achieve the average price over a horizon:

target_volume = DailyVolume * 0.05 / HoursTraded   

for t in trading_period:
   slice = max(target_volume, target_percent * remaining_volume) 
   execute(slice)

Time Weighted Average Price (TWAP) evenly splits an order over fixed intervals:

interval_length = trading_period / number_slices  

for t in trading_period:
   slice = remaining_shares / intervals_left  
   time.sleep(interval_length)
   execute(slice) 

Such algos reduce signaling trade intentions to the market.

Transaction Cost Analysis

We should explicitly model costs like slippage and optimize strategies accordingly:

# Costs as a percentage of trade value 
c = 0.1% 

def net_return(strategy, costs=c):
    return strategy.returns - (strategy.position_sizes * costs)  

Analyzing returns net of all expenses improves realism.

Optimal Scheduling

Orders can be scheduled intelligently across the day to exploit liquidity dynamics

from pandas.tseries.offsets import *

schedule = [
   (09:30, BTO, 1000),  
   (10:30, BTC, 1500),
   (14:30, STO, 2000),
]

for timestamp, side, qty in schedule:
   place_order(timestamp, side, qty)

Understanding intraday volume profiles allows timing orders to reduce costs.

As we can see, Python provides great support for even quite complex execution logic. Now let‘s discuss infrastructure and scale.

Cloud Computing and Scaling

While so far we focused on a single machine, real-world quant systems are cloud-based distributed applications processing vast amounts of data:

Cloud Infrastructure

Cloud platforms like AWS provide dynamically scalable and optimized hardware for running such systems:

import boto3
s3 = boto3.client(‘s3‘)
s3.download_file(‘bucket‘, ‘file.csv‘, ‘data.csv‘)
df = pd.read_csv(‘data.csv‘)

Services like S3 allow storing/accessing large datasets.

High Frequency Data

For intraday tick-by-tick strategies, cloud data lakes enable low latency delivery:

from azure.kusto.data import KustoClient, KustoConnectionStringBuilder
from azure.kusto.data.helpers import dataframe_from_result_table

kcsb = KustoConnectionStringBuilder("https://ingest-{cluster}.kusto.windows.net")
client = KustoClient(kcsb)
query = """ingest hftimeseries | take 10"""
df = dataframe_from_result_table(client.execute(query))

This allows pulling real-time data from sources like Azure Data Explorer.

Distributed Computing

Python workflows can be spread across servers with frameworks like Ray:

import ray

@ray.remote 
def backtest_model(param):   
    return fitness(param)

ray.init(num_cpus=10)
results = [backtest_model.remote(p) for p in params]  
best = ray.get(max(results))

Ray handles parallelization and concurrency under the hood.

So in summary, Python and associated cloud infrastructure provide all the capabilities to build institutional grade trading systems.

Now that we have covered strategy design, modeling and technology implementation, let‘s discuss risk management.

Risk Management Best Practices

While generating returns is important, managing risks is what keeps you solvent over long periods. Some key aspects are:

Statistical Risk Measurement

Instead of variance, more robust metrics of dispersion like Conditional Value at Risk should be used:

from pyportfolioopt import risk_models
returns = pd.Series(...)
model = risk_models.CovarianceShrinkage(returns)
print(model.calculate_cvar())

This provides better tail loss assessment.

Drawdown Analysis

Understanding periods of equity curve declines is needed for capital preservation:

import drawdown as dd

# Calculate drawdowns   
drawdown, high_water = dd.drawdown(returns)

# Analyze stringency
dd.annual_duration(drawdown)   

Drawdown statistics facilitate risk budgeting.

Position Sizing

Bet size should be calibrated to account for volatility clustering and extreme moves:

from pyfolio import risk

# Estimate volatility 
vol = risk.annual_volatility(returns)  

# Size positions
max_quantity = risk_budget / vol 

Dynamically adjusting position limits manages risk across regimes.

Portfolio Optimisation

Constructing portfolios help cushion strategies through diversification:

from pyportfolioopt import EfficientFrontier
from pypfopt import risk_models  

# Optimize portfolio allocation
ef = EfficientFrontier(returns, weight_bounds=(0, 1))  
ef.min_volatility()
weights = ef.clean_weights()

Optimization provides balanced exposures.

So in summary, Python has great libraries for addressing all aspects of risk management.

Conclusion

In this detailed guide, we have walked through all the components comprising an algorithmic trading system in Python, including:

1. Getting market data
2. Developing trading strategies
3. Backtesting strategy logic
4. Constructing machine learning models
5. Optimizing order execution
6. Leveraging cloud infrastructure
7. Managing portfolio risk

Of course many complexities around real world performance, latency, regulations etc remain. However, I hope this tutorial provided a solid starting point for you to start building your own high frequency trading systems using Python.

The key advantage is Python‘s vast ecosystem of open source libraries allows rapid development of complex strategies and analytical workflows with minimal coding.

Let me know if you have any other specific aspects you would like me to cover in future posts. Also check out the other topics on my blog around algorithmic trading, derivatives pricing, statistics, and programming.

Happy quant trading!