# How To Be a Quant Trader – Experiments with QuantConnect

Contributor:
Robot Wealth
Visit: Robot Wealth

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Code along Robot Wealth as they present an analysis of the SPY returns process using the QuantConnect research platform.

Excerpt

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()
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.

history[‘returns’].describe()

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’)
plt.legend()
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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