Python for Finance - Tutoriel de trading algorithmique pour les débutants

La technologie est devenue un atout en finance. Les institutions financières évoluent maintenant vers des entreprises technologiques plutôt que de rester simplement occupées par les aspects financiers du domaine.

Les algorithmes mathématiques apportent innovation et rapidité. Ils peuvent nous aider à acquérir un avantage concurrentiel sur le marché.

La vitesse et la fréquence des transactions financières, ainsi que les grands volumes de données, ont attiré beaucoup d'attention vers la technologie de toutes les grandes institutions financières.

Le trading algorithmique ou quantitatif est le processus de conception et de développement de stratégies de trading basées sur des analyses mathématiques et statistiques. C'est un domaine de la finance extrêmement sophistiqué.

Ce tutoriel sert de guide du débutant au trading quantitatif avec Python. Vous trouverez cet article très utile si vous êtes:

  1. Un étudiant ou une personne souhaitant devenir analyste quantitatif (quant) dans un fonds ou une banque.
  2. Quelqu'un qui envisage de démarrer sa propre entreprise de trading quantitatif.

Nous aborderons les sujets suivants dans cet article:

  • Bases des actions et du trading
  • Extraction des données de l'API Quandl
  • Analyse exploratoire des données sur les données de prix des actions
  • Moyennes mobiles
  • Formuler une stratégie de trading avec Python
  • Visualiser la performance de la stratégie

Avant de plonger dans les détails et la dynamique des données de prix des actions, nous devons d'abord comprendre les bases de la finance. Si vous connaissez la finance et le fonctionnement du trading, vous pouvez ignorer cette section et cliquer ici pour passer à la suivante.

Que sont les actions? Qu'est-ce que le trading d'actions?

Stocks

Une action est une représentation d'une part de la propriété d'une société, qui est émise à un certain montant. Il s'agit d'un type de garantie financière qui établit votre créance sur les actifs et la performance d'une entreprise.

Une organisation ou une entreprise émet des actions pour lever plus de fonds / capital afin de se développer et de s'engager dans plus de projets. Ces actions sont ensuite accessibles au public et sont vendues et achetées.

Trading d'actions et stratégie de trading

Le processus d'achat et de vente d'actions existantes et émises précédemment est appelé négociation d'actions. Il existe un prix auquel un stock peut être acheté et vendu, et celui-ci continue de fluctuer en fonction de la demande et de l'offre sur le marché des actions.

En fonction de la performance et des actions de l'entreprise, le cours des actions peut monter et descendre, mais le mouvement du cours de l'action ne se limite pas à la performance de l'entreprise.

Les commerçants paient de l'argent en échange de la propriété au sein d'une entreprise, dans l'espoir de faire des transactions rentables et de vendre les actions à un prix plus élevé.

Une autre technique importante suivie par les traders est la vente à découvert. Cela implique d'emprunter des actions et de les vendre immédiatement dans l'espoir de les racheter plus tard à un prix inférieur, de les rendre au prêteur et de dégager une marge.

Ainsi, la plupart des commerçants suivent un plan et un modèle pour négocier. Ceci est connu comme une stratégie de trading.

Les traders quantitatifs des hedge funds et des banques d'investissement conçoivent et développent ces stratégies et cadres de trading pour les tester. Cela nécessite une profonde expertise en programmation et une compréhension des langages nécessaires pour construire votre propre stratégie.

Python est l'un des langages de programmation les plus utilisés, parmi lesquels C ++, Java, R et MATLAB. Il est largement adopté dans tous les domaines, en particulier dans la science des données, en raison de sa syntaxe facile, de sa vaste communauté et de son support par des tiers.

Vous devrez vous familiariser avec Python et les statistiques pour tirer le meilleur parti de ce didacticiel. Assurez-vous de rafraîchir votre Python et de vérifier les principes de base des statistiques.

Extraire des données de l'API Quandl

Afin d'extraire les données de prix des actions, nous utiliserons l'API Quandl. Mais avant cela, mettons en place l'environnement de travail. Voici comment:

  1. Dans votre terminal, créez un nouveau répertoire pour le projet (nommez-le comme vous le souhaitez):
mkdir 
  1. Assurez-vous que Python 3 et virtualenv sont installés sur votre machine.
  2. Créez un nouveau virtualenv Python 3 à l'aide de virtualenv et activez-le à l'aide de source /bin/activate.
  3. Maintenant, installez jupyter-notebook en utilisant pip et tapez pip install jupyter-notebookdans le terminal.
  4. De même, installez les pandas, quandlet les numpypaquets.
  5. Exécutez votre jupyter-notebookdepuis le terminal.

Maintenant, votre notebook devrait fonctionner sur localhost comme la capture d'écran ci-dessous:

Vous pouvez créer votre premier carnet en cliquant sur le Newmenu déroulant à droite. Assurez-vous d'avoir créé un compte sur Quandl. Suivez les étapes mentionnées ici pour créer votre clé API.

Une fois que vous êtes prêt, plongeons directement dans:

# importing required packages
import pandas as pd import quandl as q

Pandas sera le package le plus rigoureusement utilisé dans ce tutoriel car nous allons faire beaucoup de manipulation et de traçage de données.

Une fois les packages importés, nous ferons des requêtes à l'API Quandl en utilisant le package Quandl:

# set the API key q.ApiConfig.api_key = "”
#send a get request to query Microsoft's end of day stock prices from 1st #Jan, 2010 to 1st Jan, 2019 msft_data = q.get("EOD/MSFT", start_date="2010-01-01", end_date="2019-01-01")
# look at the first 5 rows of the dataframe msft_data.head()

Here we have Microsoft’s EOD stock pricing data for the last 9 years. All you had to do was call the get method from the Quandl package and supply the stock symbol, MSFT, and the timeframe for the data you need.

This was really simple, right? Let’s move ahead to understand and explore this data further.

Exploratory Data Analysis on Stock Pricing Data

With the data in our hands, the first thing we should do is understand what it represents and what kind of information it encapsulates.

Printing the DataFrame’s info, we can see all that it contains:

As seen in the screenshot above, the DataFrame contains DatetimeIndex, which means we’re dealing with time-series data.

An index can be thought of as a data structure that helps us modify or reference the data. Time-series data is a sequence of snapshots of prices taken at consecutive, equally spaced intervals of time.

In trading, EOD stock pricing data captures the movement of certain parameters about a stock, such as the stock price, over a specified period of time with data points recorded at regular intervals.

Important Terminology

Looking at other columns, let’s try to understand what each column represents:

  • Open/Close — Captures the opening/closing price of the stock
  • Adj_Open/Adj_Close — An adjusted opening/closing price is a stock’s price on any given day of trading that has been revised to include any dividend distributions, stock splits, and other corporate actions that occurred at any time before the next day’s open.
  • Volume — It records the number of shares that are being traded on any given day of trading.
  • High/Low — It tracks the highest and the lowest price of the stock during a particular day of trading.

These are the important columns that we will focus on at this point in time.

We can learn about the summary statistics of the data, which shows us the number of rows, mean, max, standard deviations, and so on. Try running the following line of code in the Ipython cell:

msft_data.describe()

resample()

Pandas’ resample() method is used to facilitate control and flexibility on the frequency conversion of the time series data. We can specify the time intervals to resample the data to monthly, quarterly, or yearly, and perform the required operation over it.

msft_data.resample('M').mean()

This is an interesting way to analyze stock performance in different timeframes.

Calculating returns

Un retour financier est simplement l'argent gagné ou perdu sur un investissement. Un rendement peut être exprimé nominalement comme la variation du montant d'un investissement au fil du temps. Il peut être calculé comme le pourcentage dérivé du rapport bénéfice / investissement.

Nous avons le pct_change () à notre disposition à cet effet. Voici comment calculer les retours:

# Import numpy package import numpy as np
# assign `Adj Close` to `daily_close` daily_close = msft_data[['Adj_Close']]
# returns as fractional change daily_return = daily_close.pct_change()
# replacing NA values with 0 daily_return.fillna(0, inplace=True)
print(daily_return)

Cela imprimera les rendements que le stock a générés quotidiennement. Multiplier le nombre par 100 vous donnera le pourcentage de changement.

La formule utilisée dans pct_change () est:

Rendement = {(Prix à t) - (Prix à t-1)} / {Prix à t-1}

Maintenant, pour calculer les rendements mensuels, tout ce que vous avez à faire est:

mdata = msft_data.resample('M').apply(lambda x: x[-1]) monthly_return = mdata.pct_change()

Après avoir rééchantillonné les données en mois (pour les jours ouvrables), nous pouvons obtenir le dernier jour de négociation du mois en utilisant la apply()fonction.

apply() takes in a function and applies it to each and every row of the Pandas series. The lambda function is an anonymous function in Python which can be defined without a name, and only takes expressions in the following format:

Lambda: expression

For example, lambda x: x * 2 is a lambda function. Here, x is the argument and x * 2 is the expression that gets evaluated and returned.

Moving Averages in Trading

The concept of moving averages is going to build the base for our momentum-based trading strategy.

In finance, analysts often have to evaluate statistical metrics continually over a sliding window of time, which is called moving window calculations.

Let’s see how we can calculate the rolling mean over a window of 50 days, and slide the window by 1 day.

rolling()

This is the magical function which does the tricks for us:

# assigning adjusted closing prices to adj_pricesadj_price = msft_data['Adj_Close']
# calculate the moving average mav = adj_price.rolling(window=50).mean()
# print the resultprint(mav[-10:])

You’ll see the rolling mean over a window of 50 days (approx. 2 months). Moving averages help smooth out any fluctuations or spikes in the data, and give you a smoother curve for the performance of the company.

We can plot and see the difference:

# import the matplotlib package to see the plot import matplotlib.pyplot as plt adj_price.plot()

You can now plot the rolling mean():

mav.plot()

And you can see the difference for yourself, how the spikes in the data are consumed to give a general sentiment around the performance of the stock.

Formulating a Trading Strategy

Here comes the final and most interesting part: designing and making the trading strategy. This will be a step-by-step guide to developing a momentum-based Simple Moving Average Crossover (SMAC) strategy.

Momentum-based strategies are based on a technical indicator that capitalizes on the continuance of the market trend. We purchase securities that show an upwards trend and short-sell securities which show a downward trend.

The SMAC strategy is a well-known schematic momentum strategy. It is a long-only strategy. Momentum, here, is the total return of stock including the dividends over the last n months. This period of n months is called the lookback period.

There are 3 main types of lookback periods: short term, intermediate-term, and long term. We need to define 2 different lookback periods of a particular time series.

A buy signal is generated when the shorter lookback rolling mean (or moving average) overshoots the longer lookback moving average. A sell signal occurs when the shorter lookback moving average dips below the longer moving average.

Now, let’s see how the code for this strategy will look:

# step1: initialize the short and long lookback periods short_lb = 50long_lb = 120
# step2: initialize a new DataFrame called signal_df with a signal column signal_df = pd.DataFrame(index=msft_data.index)signal_df['signal'] = 0.0
# step3: create a short simple moving average over the short lookback period signal_df['short_mav'] = msft_data['Adj_Close'].rolling(window=short_lb, min_periods=1, center=False).mean()
# step4: create long simple moving average over the long lookback period signal_df['long_mav'] = msft_data['Adj_Close'].rolling(window=long_lb, min_periods=1, center=False).mean()
# step5: generate the signals based on the conditional statement signal_df['signal'][short_lb:] = np.where(signal_df['short_mav'][short_lb:] > signal_df['long_mav'][short_lb:], 1.0, 0.0) 
# step6: create the trading orders based on the positions column signal_df['positions'] = signal_df['signal'].diff()signal_df[signal_df['positions'] == -1.0]

Let’s see what’s happening here. We have created 2 lookback periods. The short lookback period short_lb is 50 days, and the longer lookback period for the long moving average is defined as a long_lb of 120 days.

We have created a new DataFrame which is designed to capture the signals. These signals are being generated whenever the short moving average crosses the long moving average using the np.where. It assigns 1.0 for true and 0.0 if the condition comes out to be false.

The positions columns in the DataFrame tells us if there is a buy signal or a sell signal, or to stay put. We're basically calculating the difference in the signals column from the previous row using diff.

And there we have our strategy implemented in just 6 steps using Pandas. Easy, wasn't it?

Now, let’s try to visualize this using Matplotlib. All we need to do is initialize a plot figure, add the adjusted closing prices, short, and long moving averages to the plot, and then plot the buy and sell signals using the positions column in the signal_df above:

# initialize the plot using plt fig = plt.figure()
# Add a subplot and label for y-axis plt1 = fig.add_subplot(111, ylabel="Price in $")
msft_data['Adj_Close'].plot(ax=plt1,, lw=2.)
# plot the short and long lookback moving averages signal_df[['short_mav', 'long_mav']].plot(ax=plt1, lw=2., figsize=(12,8))
# plotting the sell signals plt1.plot(signal_df.loc[signal_df.positions == -1.0].index, signal_df.short_mav[signal_df.positions == -1.0],'v', markersize=10,)
# plotting the buy signals plt1.plot(signal_df.loc[signal_df.positions == 1.0].index, signal_df.short_mav[signal_df.positions == 1.0], '^', markersize=10,) # Show the plotplt.show()

Running the above cell in the Jupyter notebook would yield a plot like the one below:

Now, you can clearly see that whenever the blue line (short moving average) goes up and beyond the orange line (long moving average), there is a pink upward marker indicating a buy signal.

A sell signal is denoted by a black downward marker where there’s a fall of the short_mav below long_mav.

Visualize the Performance of the Strategy on Quantopian

Quantopian is a Zipline-powered platform that has manifold use cases. You can write your own algorithms, access free data, backtest your strategy, contribute to the community, and collaborate with Quantopian if you need capital.

We have written an algorithm to backtest our SMA strategy, and here are the results:

Here is an explanation of the above metrics:

  • Total return: The total percentage return of the portfolio from the start to the end of the backtest.
  • Specific return: The difference between the portfolio’s total returns and common returns.
  • Common return: Returns that are attributable to common risk factors. There are 11 sector and 5 style risk factors that make up these returns. The Sector Exposure and Style Exposure charts in the Risk section provide more detail on these factors.
  • Sharpe: The 6-month rolling Sharpe ratio. It is a measure of risk-adjusted investment. It is calculated by dividing the portfolio’s excess returns over the risk-free rate by the portfolio’s standard deviation.
  • Max Drawdown: The largest drop of all the peak-to-trough movement in the portfolio’s history.
  • Volatility: Standard deviation of the portfolio’s returns.

Pat yourself on the back as you have successfully implemented your quantitative trading strategy!

Where to go From Here?

Now that your algorithm is ready, you’ll need to backtest the results and assess the metrics mapping the risk involved in the strategy and the stock. Again, you can use BlueShift and Quantopian to learn more about backtesting and trading strategies.

Further Resources

Quantra is a brainchild of QuantInsti. With a range of free and paid courses by experts in the field, Quantra offers a thorough guide on a bunch of basic and advanced trading strategies.

  • Data Science Course — They have rolled out an introductory course on Data Science that helps you build a strong foundation for projects in Data Science.
  • Trading Courses for Beginners — From momentum trading to machine and deep learning-based trading strategies, researchers in the trading world like Dr. Ernest P. Chan are the authors of these niche courses.

Free Resources

To learn more about trading algorithms, check out these blogs:

  • Quantstart — they cover a wide range of backtesting algorithms, beginner guides, and more.
  • Investopedia — everything you want to know about investment and finance.
  • Quantivity — detailed mathematical explanations of algorithms and their pros and cons.

Warren Buffet says he reads about 500 pages a day, which should tell you that reading is essential in order to succeed in the field of finance.

Embark upon this journey of trading and you can lead a life full of excitement, passion, and mathematics.

Data Science with Harshit

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