Code along Robot Wealth as they present an analysis of the SPY returns process using the QuantConnect research platform.
Example Research With QuantConnect Code
Using the QuantConnect ecosystem in a typical quant workflow.
Note: This code is meant to be used within QuantConnect research environment
# Import dependecies
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use(‘ggplot’) #There is a positive correlation between chart pretiness and risk-adjusted returns
plt.rcParams[‘figure.figsize’] = [10, 7]
# QuantBook Analysis Tool # Load SPY historical data
qb = QuantBook()
spy = qb.AddEquity(“SPY”)
history = qb.History(qb.Securities.Keys, 5000, Resolution.Daily) #5000 days of SPY daily data
# Drop pandas level
history = history.reset_index().drop(‘symbol’,axis=1)
# Calculate SPY returns and fillna
history[‘returns’] = (history[‘close’].pct_change() * 100).fillna(0)
1. Analysing the return distribution
Now that we have SPY daily returns let’s quickly see what we’re dealing with.
Name: returns, dtype: float64
Let’s look at the extreme values of returns ie max and min
history[history[‘returns’] == min(history[‘returns’])]
history[history[‘returns’] == max(history[‘returns’])]
The recent corona drawdown is the biggest single-day market drop in history, and we have the biggest up move in 2008.
Let’s look at the distribution of daily returns for the SPY
sns.distplot(history[‘returns’],label=’Distribution of SPY returns’) plt.legend()
2. Comparing to a normal distribution
Let’s first create some random data and plot their distribution
random = np.random.normal(scale=1.23,size=500000)
sns.distplot(random,label=’Returns sampled from normal distribution’,color=’blue’)
random_series = pd.Series(random)
There it is, a beautiful well behaved normal distribution, Let’s see how this compares to our SPY returns distribution.
sns.distplot(history[‘returns’],label=’Distribution of SPY returns’) sns.distplot(random,label=’Returns sampled from normal distribution’) plt.legend()
Now the high kurtosis of the SPY returns becomes even more apparent.
So far we’ve learned that:
- SPY returns do resemble random returns
- but they have big tails in their distribution
- which means we can expect outsized moves to the upside and downside, more so than a normal distribution would suggest.
Now let’s look at a simple workflow for researching, seasonal patterns in our financial data.
Visit Robot Wealth to read the next steps Researching possible seasonal patterns and Looking for auto-correlation (trend) in the return process, and to download the sample code: https://robotwealth.com/how-to-be-a-quant-trader-experiments-with-quantconnect/
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