Prototyping Trading Strategies with Python¶
Getting Started with Python for Algorithmic Trading¶
© Ran Aroussi
@aroussi | aroussi.com | github.com/ranaroussi
February, 2017
About me¶
History¶
- Programming since forever
- Operating in Online / AdTech space since 1997
- Trading since 2013
- Switched to automated trading in 2014
Co-Founder of Native Alpha¶
Trading mostly Futures and Options using automated and semi-automated strategies, with a 100% technical / quantitative approach
Developer of ezIBpy¶
A Pythonic wrapper for the IbPy library, that ease the communication with Interactive Brokers for market data and order execution.
Available via github.com/ranaroussi/ezibpy
Developer of QTPyLib¶
A simple, event-driven, algorithmic trading system written in Python, that supports backtesting and live trading using Interactive Brokers for market data and order execution (QTPyLib stands for: Quantitative Trading Python Library).
Available via github.com/ranaroussi/qtpylib
Agenda¶
- Why Pyhton for Algorithmic Trading?
- The Python Toolbox: Platforms and Libraries for Trading
- Data Wrangling and Working with Market Data
- Using Technical Indicators with Python
- Coming Up with Trading Ideas
- Writing and Testing your first Trading Strategy
- Strategy Development Workflow
* This is the first webinar of a multi webinar series
Why Python?¶
Python's Benifits¶
- A very high-level language with clean and readable syntax
- Fast learning curve
- Object-oriented, multi-paradigm
- A rich standard library of modules
- One of the most used languages in IoT, data science, finance, and web development
- A strong open-source community
"Hello World" in Python¶
In [ ]:
print("hello world")
Compared to C#...¶
In [ ]:
public class Hello
{
public static void Main()
{
System.Console.WriteLine("Hello, World!");
}
}
Why Python for Trading?¶
Python for Algorithmic Trading¶
- Python's syntax is attractive for science, and particularly finance
- Fast learning curve
- Solid Machine Learning libraries
- Quick protopyting, fast enough execution
- Many Algorithmic Trading Libraries and Tools (Zipline, PyAlgoTrade, PyBacktest, QTPyLib)
- Pandas!
The Python Toolbox¶
Recommended Libraries¶
- TA-Lib - technical indicators
- scikit-learn - machine learning algorithms
- Tensorflow - deep learning / neural network
- SciPy - scientific functions
- statsmodels - statistical functions
- SQLAlchemy - relational database abstraction library
- TsTables - tick data storage and retrieval
- ezIBpy - Interactive Brokers bridge
- OandaPy - Oanda bridge
Python 2 or Python 3?¶
Python 3!¶
Data Wrangling and Working with Market Data¶
Pandas - the times series and tabular data wrangler¶
- Pandas is one of the best libraries (in any language, IMO) for data exploration and manipulation
- Main objects are
DataFrameandSeries - Has many available built-in operations
In [3]:
import pandas as pd
import numpy as np
from pandas_datareader import data
spy = data.get_data_yahoo("SPY", start="2000-01-01")
spy.head()
Out[3]:
In [4]:
ratio = spy["Close"] / spy["Adj Close"]
spy["close"] = spy["Adj Close"]
spy["open"] = spy["Open"] / ratio
spy["high"] = spy["High"] / ratio
spy["low"] = spy["Low"] / ratio
spy["volume"] = spy["Volume"]
spy = spy[['open','high','low','close','volume']]
spy.head()
Out[4]:
In [5]:
spy.to_csv('~/Desktop/sp500_ohlc.csv')
In [6]:
spy.shape
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In [7]:
# calculate returns
spy['return'] = spy['close'].pct_change()
spy['return'].describe()
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In [8]:
# slicing
spy[5:10][["close", "return"]]
Out[8]:
In [9]:
spy[ spy['return'] > 0.005 ]['return'].describe()
Out[9]:
In [10]:
spy['close'].plot()
Out[10]:
In [11]:
spy['return'].plot.hist(bins=100, edgecolor='white')
Out[11]:
In [12]:
# resampling
spy['return'].resample("1A").sum() * 100
Out[12]:
Using Technical Indicators with Python¶
In [35]:
# pandas' rolling mean
spy['ma1'] = spy['close'].rolling(window=50).mean()
spy['ma2'] = spy['close'].rolling(window=200).mean()
In [13]:
spy[['ma1', 'ma2', 'close']].plot(linewidth=1)
Out[13]:
In [14]:
# rolling standard deviation
spy['return'].rolling(window=20).std().plot()
Out[14]:
In [15]:
# Calculating log returns and volatility
spy['logret'] = np.log(spy['close'] / spy['close'].shift(1))
spy['volatility'] = spy['logret'].rolling(window=252).std() * np.sqrt(252)
spy[['close', 'volatility']].plot(subplots=True)
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In [17]:
# pure python bollinger bands
spy['sma'] = spy['close'].rolling(window=20).mean()
spy['std'] = spy['close'].rolling(window=20).std()
spy['upperbb'] = spy['sma'] + (spy['std'] * 2)
spy['lowerbb'] = spy['sma'] - (spy['std'] * 2)
plot_candlestick(spy[-100:])
plt.plot(spy[-100:][['upperbb', 'sma', 'lowerbb']], linewidth=1)
Out[17]:
In [36]:
# using TA-Lib..
import talib as ta
spy['rsi'] = ta.RSI(spy['close'].values, timeperiod=2)
spy[-100:][['close', 'rsi']].plot(subplots=True)
Out[36]: