1. System Overview¶

TradingBot is a python program with the goal to automate the trading of stocks in the London Stock Exchange market. It is designed around the idea that to trade in the stock market you need a strategy: a strategy is a set of rules that define the conditions where to buy, sell or hold a certain market. TradingBot design lets the user implement a custom strategy without the trouble of developing all the boring stuff to make it work.

The following sections give an overview of the main components that compose TradingBot.

1.1. TradingBot¶

TradingBot is the main entiy used to initialised all the components that will be used during the main routine. It reads the configuration file and the credentials file, it creates the configured strategy instance, the broker interface and it handle the processing of the markets with the active strategy.

1.2. Broker interface¶

TradingBot requires an interface with an executive broker in order to open and close trades in the market. The broker interface is initialised in the TradingBot module and it should be independent from its underlying implementation.

At the current status, the only supported broker is IGIndex. This broker provides a very good set of API to analyse the market and manage the account. TradingBot makes also use of other 3rd party services to fetch market data such as price snapshot or technical indicators.

1.3. Strategy¶

The Strategy is the core of the TradingBot system. It is a generic template class that can be extended with custom functions to execute trades according to the personalised strategy.

1.3.1. How to use your own strategy¶

Anyone can create a new strategy from scratch in a few simple steps. With your own strategy you can define your own set of rules to decide whether to buy, sell or hold a specific market.

Setup your development environment (see README.md)
Create a new python module inside the Strategy folder :
```
cd Strategies
touch my_strategy.py
```

Edit the file and add a basic strategy template like the following:

import os
import inspect
import sys
import logging

# Required for correct import path
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(currentdir)
sys.path.insert(0,parentdir)

from Components.Utils import Utils, Interval, TradeDirection
from .Strategy import Strategy
# Import any other required module

class my_strategy(Strategy): # Extends Strategy module
    """
    Description of the strategy
    """
    def read_configuration(self, config):
        # Read from the config json and store config parameters
        pass

    def initialise(self):
        # Initialise the strategy
        pass

    def fetch_datapoints(self, market):
        """
        Fetch any required datapoints (historic prices, indicators, etc.).
        The object returned by this function is passed to the 'find_trade_signal()'
        function 'datapoints' argument
        """
        # As an example, this means the strategy needs 50 data point of
        # of past prices from the 1-hour chart of the market
        return self.broker.get_prices(market.epic, Interval.HOUR, 50)

    def find_trade_signal(self, market, prices):
        # Here is where you want to implement your own code!
        # The market instance provide information of the market to analyse while
        # the prices dictionary contains the required price datapoints
        # Returns the trade direction, stop level and limit level
        # As an examle:
        return TradeDirection.BUY, 90, 150

    def backtest(self, market, start_date, end_date):
        # This is still a work in progress
        # The idea here is to perform a backtest of the strategy for the given market
        return {"balance": balance, "trades": trades}

Add the implementation for these functions:
- read_configuration: config is the json object loaded from the config.json file
- initialise: initialise the strategy or any internal members
- fetch_datapoints: fetch the required past price datapoints
- find_trade_signal: it is the core of your custom strategy, here you can use the broker interface to decide if trade the given epic
- backtest: backtest the strategy for a market within the date range
Strategy parent class provides a Broker type internal member that can be accessed with self.broker. This member is the TradingBot broker interface and provide functions to fetch market data, historic prices and technical indicators. See the Modules section for more details.
Strategy parent class provides access to another internal member that list the current open position for the configured account. Access it with self.positions.
Edit the StrategyFactory module inporting the new strategy and adding its name to the StrategyNames enum. Then add it to the make function
Edit the config.json adding a new section for your strategy parameters
Create a unit test for your strategy
Share your strategy creating a Pull Request :)