Algorithmic Trading Bot Using Python

Trading online has become one of the most popular investment in the current world. The likes of cryptocurrency and forex being the leading areas. Due to this popularity, programmers have emerged trying to come up with a way in which the trading process can be automated for more profits. <!--more--> In this tutorial, we will look at how one can start off his/her journey in programming a trading bot.

Table of contents #

• What is a trading bot
• Prerequisites
• Coding and development
• Why algorithmic Trading Bot
• Why are people still against algorithmic trading?
• Conclusion

What is a trading bot? #

A trading bot is simply a software that automates the trading process. It uses past data to give out expected outcomes.

Prerequisites #

The main prerequisite for this tutorial is basic knowledge of python and its algorithms. For testing, we will use QUANTCONNECT which uses the lean engine to integrate your code with the trading site. That means that you don't require an offline editor since the site provides its own development environment.

Coding and development #

With your requirements ready, you can now code along for a practical understanding.

Go to www.quantconnect.com and sign up to set up your coding environment. You can also use an offline editor and upload the code later for testing.

Let's get started!

We will develop the bot by following the steps below:

Step 1: Create a new algorithm #

From the options on the left side of the page, click on Create new Algorithm. You will be directed to the editor with a class generated automatically.

Below is the class generated:

class GeekyBlueSeahorse(QCAlgorithm):
    def Initialize(self):
    def OnData(self, data):

Step 2: Import required libraries #

In this case, we will only require one library i.e. NumPy.

Import at the top as follows:

import NumPy as np

Step 3. Initialize required variables #

Under the initialize method, we will initialize several parameters:

• Initialize cash for the purpose of the backtest (we call it the strategy cash) which would be used on a real account.
• Set the start and end date for the backtest.

The code is as follows:

        self.SetStartDate(2015, 3, 26)  # Set Start Date
        self.SetEndDate(2021, 9, 25) # Set End Date
        self.SetCash(100000)  # Set Strategy Cash

Still under the initialize method, we will:

• Use AddEquity function to monitor the resolution of the intended data.
• Initialize the amount of days we will look back to determine our break point.
• Set limits for the lookback i.e. a lower and an upper limit

In this case, we will use a daily resolution:

    self.symbol = self.AddEquity("SPY", Resolution.Daily).Symbol
    self.lookback = 20
    self.ceiling, self.floor = 30, 10

The last thing to initialize is the stop loss extent:

    self.initialStopRisk = 0.98
    self.trailingStopRisk = 0.9

The first variable determines how close our stop loss will be to the security price. Meaning that it will allow a 2% loss before it gets hit.

The second variable indicates how close our trading stop will follow the assets' price. This means that it will trail the price for 10% which is quite big but gives more room for price flexibility.

Step 4: Define a method to plot the data #

We will define the onData method to create a plot of the price of the securities. This gives a benchmark to compare our algorithm performance:

def OnData(self, data):
       self.Plot("Data Chart", self.symbol, self.Securities[self.symbol].Close)

This will also determine the closing price.

Step 5:Create the trading method #

The next step will be to create the method that will do all the trading for us. It will be called after a market opens. We will call it "EveryMarketOpen" for simplicity.

def EveryMarketOpen(self):

After this, we will have to initialize one more function in the initialize method. This the Schedule.On function which takes three parameters:

• The first specifies on which day the method is called.
• The second specifies at which time the method is called
• The last specifies which method is called, in this case it is EveryMarketOpen method.

Add the following code to the initialize method:

self.Schedule.On(self.DateRules.EveryDay(self.symbol), \
                self.TimeRules.AfterMarketOpen(self.symbol, 20), \
                Action(self.EveryMarketOpen))

Step 6: Implement the EveryMarketOpen method #

First, we will determine the lookback length for our breakout. Within a utility of 60 days, we will compare the current value today with the same value yesterday. This will help determine the length of the lookback window.

Call the History function to get data for the last 61 days or your preferred number of days. This is where we use the NumPy library to calculate the standard deviation for the two days. We will list all the highest and lowest prices within a specified range, for this case, 60days

The following code falls under this EveryMarketOpen method to perform all the comparisons required to give a result:

    def EveryMarketOpen(self):
        close = self.History(self.Symbol, 61, Resolution.Monthly)["close"]
        todayvol = np.std(close[1:61])
        yesterdayvol = np.std(close[0:60]) // standard deviation
        initialvol = (currentvol - yestervol) / (currentvol)

        self.lookback = round(self.lookback * (1 + initialvol))

        if self.lookback > self.highest:
            self.lookback = self.highest
        elif
          self.lookback < self.lowest:
            self.lookback = self.lowest
        self.high = self.History(self.Symbol, self.lookback, Resolution.Minute)["high"]

        if not self.Securities(self.Symbol).Invested and \
                self.Securities(self.Symbol).Close >= max(self.high[:-1]):

                self.SetHoldings(self.Symbol, 1)
                self.breakoutlevel = max(selt.high[:-1])
                self.highestPrice = self.breakoutlevel

        if self.Securities(self.Symbol).Invested:

            if not self.Transactions.GetOpenOrders(self.symbol):
                self.endMarketTicket = self.endMarketOrder(self.symbol, \
                                        -self.Portfolio[self.symbol].Quantity, \
                                        self.initialStopRisk * self.breakoutlevel)


            if not self.Securities(self.Symbol).Close > self.highestPrice and \
                    self.initialStopRisk * self.breakoutlevel < self.Securities[self.symbol].Close * self.trailingStopRisk:

                    self.highestPrice = self.Securities[self.symbol].Close
                    updateFields = UpdateOrderFields()
                    updateFields.stopPrice = self.Securities[self.Symbol] * self.trailingStopRisk
                    self.endMarketTicket.Update(updateFields)

Step 7: Plotting the stop price #

Lastly, print the new stock price to the console, to check the new order price every time they get updated. We cannot use the normal print function, instead, use the self.Debug function which is quant equivalent to print.

Finalize by plotting the stop price of our position onto the data chart we created earlier. This allows us to view where our stop price is compared to the securities trading price.

self.Debug(updateFields.stopPrice)
        self.Plot("Data Chart", "Stop Price", self.stopMarketTicket.Get(OrderField.StopPrice))

Below is how the complete code looks like:

import numpy as np
class GeekyBlueSeahorse(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2015, 3, 26)  # Set Start Date
        self.SetEndDate(2021, 9, 25)
        self.SetCash(100000)  # Set Strategy Cash
        self.symbol = self.AddEquity("SPY", Resolution.Daily).Symbol
        self.lookback = 20
        self.ceiling, self.floor = 30, 10
        self.initialStopRisk = 0.98
        self.trailingStopRisk = 0.9

        self.Schedule.On(self.DateRules.EveryDay(self.symbol), \
                         self.TimeRules.AfterMarketOpen(self.symbol, 20), \
                          Action(self.EveryMarketOpen))
    def OnData(self, data):
        self.Plot("Data Chart", self.symbol, self.Securities[self.symbol].Close)

 def EveryMarketOpen(self):
        close = self.History(self.Symbol, 61, Resolution.Monthly)["close"]
        todayvol = np.std(close[1:61])
        yesterdayvol = np.std(close[0:60]) // standard deviation
        initialvol = (currentvol - yestervol) / (currentvol)

        self.lookback = round(self.lookback * (1 + initialvol))

        if self.lookback > self.highest:
            self.lookback = self.highest
        elif
          self.lookback < self.lowest:
            self.lookback = self.lowest
        self.high = self.History(self.Symbol, self.lookback, Resolution.Minute)["high"]

        if not self.Securities(self.Symbol).Invested and \
                self.Securities(self.Symbol).Close >= max(self.high[:-1]):
                self.SetHoldings(self.Symbol, 1)
                self.breakoutlevel = max(selt.high[:-1])
                self.highestPrice = self.breakoutlevel
        if self.Securities(self.Symbol).Invested:
            if not self.Transactions.GetOpenOrders(self.symbol):
                self.endMarketTicket = self.endMarketOrder(self.symbol, \
                                        -self.Portfolio[self.symbol].Quantity, \
                                        self.initialStopRisk * self.breakoutlevel)
        if not self.Securities(self.Symbol).Close > self.highestPrice and \
                    self.initialStopRisk * self.breakoutlvl < self.Securities[self.symbol].Close * self.trailingStopRisk:
                    self.highestPrice = self.Securities[self.symbol].Close
                    updateFields = UpdateOrderFields()
                    updateFields.stopPrice = self.Securities[self.Symbol] * self.trailingStopRisk
                    self.endMarketTicket.Update(updateFields)
                    self.Debug(updateFields.stopPrice)
                    self.Plot("Data Chart", "Stop Price", self.endMarketTicket.Get(OrderField.StopPrice))

Refer to this video for more information.

Step 8: Performing a backtest #

A backtest is performed to evaluate the performance of the algorithm. It integrates the code with your brokerage site to get results as if it was an actual trading practice.

You can see the image below:

According to the values you entered, you should get your results in a similar interface to the one shown in the image below:

Why algorithmic trading bot #

Here are some of the key merits of this algorithmic trading:

1. Saves on time since a trader does not need to sit there all day doing the trade. This allows one to work on other things while earning.
2. Higher accuracy expected since the bot compares and considers a large volume of data before coming up with a prediction. This is a bonus since a trader cannot go through data of up to or maybe more than a year.
3. Increased trading time - Most probably, the bot can trade for 24hours a day without getting tired. This means that if its accuracy is higher enough, then the profits made are higher than human-guided trading.

Why are people still against algorithmic trading? #

Besides giving better results, some people are still against this type of trading. This is the one Main reason:

Systemic Risk #

Sometimes, linkages between financial markets may occur. As a result, algorithms operating under those markets transmit rapid shocks from one market to another thus triggering a systematic risk. These systematic risks bring about huge losses, which is considered a great risk.

Due to such risk, people prefer to trade on their own rather than depending on the software.

Conclusion #

With the growing E-economy, e-trading has become one of the biggest contributors. Giving a verdict on whether to use automated software (Trading bot) or not is always a personal decision.

This article sets a journey for those who are interested in developing their algorithm for trading rather than using the ready-made.

I hope you find this tutorial helpful.

Happy coding!

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Peer Review Contributions by: Monica Masae