How To Be a Quant Trader – Experiments with QuantConnect

Articles From: Robot Wealth
Website: Robot Wealth


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‘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.


count 5000.000000
mean 0.030071
std 1.235997
min -11.638806
25% -0.443536
50% 0.061797
75% 0.573180
max 11.360371
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:

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