Python for Finance - Algoritmisk handelsvejledning til begyndere

Teknologi er blevet et aktiv inden for finansiering. Finansielle institutioner udvikler sig nu til teknologivirksomheder snarere end blot at forblive optaget af de økonomiske aspekter af feltet.

Matematiske algoritmer skaber innovation og hastighed. De kan hjælpe os med at opnå en konkurrencemæssig fordel på markedet.

Hastigheden og hyppigheden af ​​finansielle transaktioner sammen med de store datamængder har trukket stor opmærksomhed mod teknologi fra alle de store finansielle institutioner.

Algoritmisk eller kvantitativ handel er processen med at designe og udvikle handelsstrategier baseret på matematiske og statistiske analyser. Det er et uhyre sofistikeret finansområde.

Denne vejledning fungerer som nybegyndervejledning til kvantitativ handel med Python. Du finder dette indlæg meget nyttigt, hvis du er:

  1. En studerende eller en person, der sigter mod at blive en kvantitativ analytiker (quant) i en fond eller bank.
  2. En person, der planlægger at starte deres egen kvantitative handelsvirksomhed.

Vi gennemgår følgende emner i dette indlæg:

  • Grundlæggende om aktier og handel
  • Uddrag af data fra Quandl API
  • Eksplorativ dataanalyse af aktieprisdata
  • Glidende gennemsnit
  • Formulering af en handelsstrategi med Python
  • Visualisering af udførelsen af ​​strategien

Før vi dyber dybt ned i detaljerne og dynamikken i aktieprisdata, skal vi først forstå det grundlæggende inden for finansiering. Hvis du er en person, der er fortrolig med finansiering og hvordan handel fungerer, kan du springe dette afsnit over og klikke her for at gå til den næste.

Hvad er aktier? Hvad er aktiehandel?

Aktier

En aktie er en repræsentation af en andel i ejerskabet af et selskab, der udstedes til et bestemt beløb. Det er en form for økonomisk sikkerhed, der fastslår dit krav på en virksomheds aktiver og ydeevne.

En organisation eller virksomhed udsteder aktier for at skaffe flere midler / kapital for at skalere og engagere sig i flere projekter. Disse lagre er derefter offentligt tilgængelige og sælges og købes.

Aktiehandel og handelsstrategi

Processen med at købe og sælge eksisterende og tidligere udstedte aktier kaldes aktiehandel. Der er en pris, hvor en aktie kan købes og sælges, og den fortsætter med at svinge afhængigt af efterspørgslen og udbuddet på aktiemarkedet.

Afhængig af virksomhedens præstationer og handlinger kan aktiekurserne bevæge sig op og ned, men aktiekursbevægelsen er ikke begrænset til virksomhedens præstationer.

Handlende betaler penge til gengæld for ejerskab i en virksomhed i håb om at foretage nogle rentable handler og sælge aktierne til en højere pris.

En anden vigtig teknik, som handlende følger, er short selling. Dette indebærer at låne aktier og straks sælge dem i håb om at købe dem senere til en lavere pris, returnere dem til långiveren og gøre margenen.

Så de fleste handlende følger en plan og model til handel. Dette er kendt som en handelsstrategi.

Kvantitative forhandlere i hedgefonde og investeringsbanker designer og udvikler disse handelsstrategier og rammer for at teste dem. Det kræver dyb programmeringskompetence og en forståelse af de sprog, der er nødvendige for at opbygge din egen strategi.

Python er et af de mest populære programmeringssprog, der bruges, blandt lignende C ++, Java, R og MATLAB. Det vedtages bredt på tværs af alle domæner, især inden for datavidenskab, på grund af dets nemme syntaks, enorme samfund og tredjepartsstøtte.

Du har brug for fortrolighed med Python og statistik for at få mest muligt ud af denne vejledning. Sørg for at pusse op på din Python og tjekke de grundlæggende statistikker.

Uddrag af data fra Quandl API

For at udtrække aktieprisdata bruger vi Quandl API. Men inden det, lad os oprette arbejdsmiljøet. Sådan gør du:

  1. I din terminal skal du oprette en ny mappe til projektet (navngiv det, men du vil):
mkdir 
  1. Sørg for, at du har Python 3 og virtualenv installeret på din maskine.
  2. Opret en ny Python 3 virtualenv ved hjælp af virtualenv og aktiver den ved hjælp af source /bin/activate.
  3. Installer nu jupyter-notebook ved hjælp af pip, og skriv pip install jupyter-notebookterminalen.
  4. Ligeledes installere pandas, quandlog numpypakker.
  5. Kør din jupyter-notebookfra terminalen.

Nu skal din notesbog køre på localhost som skærmbilledet nedenfor:

Du kan oprette din første notesbog ved at klikke på Newrullemenuen til højre. Sørg for, at du har oprettet en konto på Quandl. Følg trinene nævnt her for at oprette din API-nøgle.

Når du er klar, lad os dykke lige ind i:

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

Pandas bliver den mest anvendte pakke i denne vejledning, da vi laver en masse datamanipulation og plotting.

Når pakkerne er importeret, sender vi anmodninger til Quandl API ved hjælp af Quandl-pakken:

# 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

Et økonomisk afkast er simpelthen de penge, der er tjent eller tabt på en investering. Et afkast kan udtrykkes nominelt som ændringen i investeringsbeløbet over tid. Det kan beregnes som den procentdel, der stammer fra forholdet mellem fortjeneste og investering.

Vi har pct_change () til vores rådighed til dette formål. Her er hvordan du kan beregne afkast:

# 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)

Dette vil udskrive de afkast, som bestanden har genereret dagligt. Hvis du multiplicerer tallet med 100, får du den procentvise ændring.

Formlen anvendt i pct_change () er:

Retur = {(Pris t) - (Pris t-1)} / {Pris t-1}

For at beregne månedlige afkast er alt hvad du skal gøre nu:

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

Efter at have samplet dataene til måneder (for hverdage) kan vi få den sidste handelsdag i måneden ved hjælp af apply()funktionen.

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:

  • Disse serier dækker alle de krævede / krævede kvalitetsvejledninger om hvert af emnerne og underemnerne som Python-grundlæggende data.
  • Forklaret matematik og afledninger af, hvorfor vi gør, hvad vi gør i ML og Deep Learning.
  • Podcasts med dataforskere og ingeniører hos Google, Microsoft, Amazon osv. Og administrerende direktører for store datadrevne virksomheder.
  • Projekter og instruktioner til implementering af de emner, der er lært indtil videre. Lær om nye certificeringer, Bootcamp og ressourcer til at knække disse certificeringer som denne TensorFlow Developer Certificate Exam fra Google.

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