La tecnología se ha convertido en un activo en las finanzas. Las instituciones financieras ahora están evolucionando hacia empresas de tecnología en lugar de simplemente permanecer ocupadas con los aspectos financieros del campo.

Los algoritmos matemáticos generan innovación y velocidad. Pueden ayudarnos a obtener una ventaja competitiva en el mercado.

La velocidad y frecuencia de las transacciones financieras, junto con los grandes volúmenes de datos, ha atraído mucha atención hacia la tecnología de todas las grandes instituciones financieras.

El comercio algorítmico o cuantitativo es el proceso de diseño y desarrollo de estrategias comerciales basadas en análisis matemáticos y estadísticos. Es un área financiera inmensamente sofisticada.

Este tutorial sirve como guía para principiantes sobre el comercio cuantitativo con Python. Encontrarás esta publicación muy útil si eres:

Un estudiante o alguien que desee convertirse en analista cuantitativo (quant) en un fondo o banco.
Alguien que esté planeando comenzar su propio negocio comercial cuantitativo.

Repasaremos los siguientes temas en esta publicación:

Conceptos básicos de acciones y comercio
Extraer datos de la API de Quandl
Análisis de datos exploratorios sobre precios de acciones.
Medias móviles
Formular una estrategia comercial con Python
Visualizar el desempeño de la estrategia

Antes de profundizar en los detalles y la dinámica de los datos de precios de las acciones, primero debemos comprender los conceptos básicos de las finanzas. Si es alguien que está familiarizado con las finanzas y cómo funciona el comercio, puede omitir esta sección y hacer clic aquí para pasar a la siguiente.

¿Qué son las acciones? ¿Qué es el comercio de acciones?

Cepo

Una acción es una representación de una acción en la propiedad de una corporación, que se emite por una determinada cantidad. Es un tipo de garantía financiera que establece su derecho sobre los activos y el desempeño de una empresa.

Una organización o empresa emite acciones para recaudar más fondos / capital con el fin de escalar y participar en más proyectos. Estas acciones están disponibles públicamente y se venden y compran.

Comercio de acciones y estrategia comercial

El proceso de compra y venta de acciones existentes y emitidas anteriormente se denomina negociación de acciones. Existe un precio al que se puede comprar y vender una acción, y este sigue fluctuando según la demanda y la oferta en el mercado de valores.

Dependiendo del desempeño y las acciones de la empresa, los precios de las acciones pueden subir y bajar, pero el movimiento del precio de las acciones no se limita al desempeño de la empresa.

Los comerciantes pagan dinero a cambio de la propiedad dentro de una empresa, con la esperanza de realizar algunas operaciones rentables y vender las acciones a un precio más alto.

Otra técnica importante que siguen los traders es la venta al descubierto. Esto implica tomar prestadas acciones y venderlas inmediatamente con la esperanza de comprarlas más tarde a un precio más bajo, devolverlas al prestamista y obtener el margen.

Entonces, la mayoría de los comerciantes siguen un plan y un modelo para operar. Esto se conoce como estrategia comercial.

Los operadores cuantitativos de los fondos de cobertura y los bancos de inversión diseñan y desarrollan estas estrategias y marcos comerciales para probarlos. Requiere una profunda experiencia en programación y una comprensión de los lenguajes necesarios para construir su propia estrategia.

Python es uno de los lenguajes de programación más utilizados, entre los que se encuentran C ++, Java, R y MATLAB. Se está adoptando ampliamente en todos los dominios, especialmente en la ciencia de datos, debido a su sintaxis fácil, su gran comunidad y el soporte de terceros.

Necesitará estar familiarizado con Python y las estadísticas para aprovechar al máximo este tutorial. Asegúrese de repasar su Python y revisar los fundamentos de las estadísticas.

Extraer datos de la API de Quandl

Para extraer datos de precios de acciones, usaremos la API de Quandl. Pero antes de eso, configuremos el entorno de trabajo. Así es cómo:

En su terminal, cree un nuevo directorio para el proyecto (asígnele el nombre que desee):

mkdir

Asegúrese de tener Python 3 y virtualenv instalados en su máquina.
Cree un nuevo virtualenv de Python 3 usando virtualenv y actívelo usando source /bin/activate.
Now, install jupyter-notebook using pip, and type in pip install jupyter-notebook in the terminal.
Similarly, install the pandas, quandl, and numpy packages.
Run your jupyter-notebook from the terminal.

Now, your notebook should be running on localhost like the screenshot below:

You can create your first notebook by clicking on the New dropdown on the right. Make sure you have created an account on Quandl. Follow the steps mentioned here to create your API key.

Once you’re all set, let’s dive right in:

# importing required packages

import pandas as pd import quandl as q

Pandas is going to be the most rigorously used package in this tutorial as we’ll be doing a lot of data manipulation and plotting.

After the packages are imported, we will make requests to the Quandl API by using the Quandl package:

# 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 rendimiento financiero es simplemente el dinero ganado o perdido en una inversión. Un rendimiento se puede expresar nominalmente como el cambio en el monto de una inversión a lo largo del tiempo. Puede calcularse como el porcentaje derivado de la relación beneficio / inversión.

Tenemos el pct_change () a nuestra disposición para este propósito. Así es como puede calcular las devoluciones:

# 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)

Esto imprimirá las devoluciones que la acción ha estado generando a diario. Multiplicar el número por 100 le dará el cambio porcentual.

La fórmula utilizada en pct_change () es:

Retorno = {(Precio en t) - (Precio en t-1)} / {Precio en t-1}

Ahora, para calcular los rendimientos mensuales, todo lo que necesita hacer es:

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

Después de volver a muestrear los datos a meses (para días hábiles), podemos obtener el último día de negociación del mes utilizando la apply()función.

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

With this channel, I am planning to roll out a couple of series covering the entire data science space. Here is why you should be subscribing to the channel:

These series would cover all the required/demanded quality tutorials on each of the topics and subtopics like Python fundamentals for Data Science.
Explained Mathematics and derivations of why we do what we do in ML and Deep Learning.
Podcasts with Data Scientists and Engineers at Google, Microsoft, Amazon, etc, and CEOs of big data-driven companies.
Projects and instructions to implement the topics learned so far. Learn about new certifications, Bootcamp, and resources to crack those certifications like this TensorFlow Developer Certificate Exam by Google.

Further on, you can connect with me on Twitter or LinkedIn.