Utilities

This section covers a variety of utilites that provide complimentary functionality to your trading algorithms. These are code snippets that you can add to any algorithm to add the desired functionality.

If you are looking for example trading algorithms, see the corresponding section.

Output to CSV file

Add this script to the analyze method to create and save a CSV file with the results from the trading algorithm. This file will include the default parameters of the results DataFrame plus any recorded variables and will be saved in the same location where your trading algorithm is saved. The exact script that you need to use depends on the interface that you are using to run your trading algorithm, which could be the CLI or a Python Interpreter.

1. Script to use with CLI:

   def analyze(context=None, results=None):
       import sys
       import os
       from os.path import basename
   
       # Save results in CSV file
       filename = os.path.splitext(basename(sys.argv[3]))[0]
       results.to_csv(filename + '.csv')
   

2. Script to use with Python Interpreter:

   def analyze(context=None, results=None):
       import os
       from os.path import basename
   
       # Save results in CSV file
       filename = os.path.splitext(os.path.basename(__file__))[0]
       results.to_csv(filename + '.csv')
   

Extracting market data

Use this script to save the price and volume data of one cryptoasset in a CSV file, which will be saved in the same location and with the same name as your Python file. To get custom data, simply modify the asset’s symbol and the dates. Run this script directly from your development environment: python scriptname.py, where the contents of ‘scriptname.py’ are as follows. Two different version are provided as an example for daily- and minute-resolution data respectively:

Simpler case for daily data

import os
import pytz
from datetime import datetime

from catalyst.api import record, symbol, symbols
from catalyst.utils.run_algo import run_algorithm

def initialize(context):
   # Portfolio assets list
   context.asset = symbol('btc_usdt') # Bitcoin on Poloniex

def handle_data(context, data):
   # Variables to record for a given asset: price and volume
   price = data.current(context.asset, 'price')
   volume = data.current(context.asset, 'volume')
   record(price=price, volume=volume)

def analyze(context=None, results=None):

   # Generate DataFrame with Price and Volume only
   data = results[['price','volume']]

   # Save results in CSV file
   filename = os.path.splitext(os.path.basename(__file__))[0]
   data.to_csv(filename + '.csv')

''' Bitcoin data is available on Poloniex since 2015-3-1.
    Dates vary for other tokens. In the example below, we choose the
    full month of July of 2017.
'''
start = datetime(2017, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2017, 7, 31, 0, 0, 0, 0, pytz.utc)
results = run_algorithm(initialize=initialize,
                        handle_data=handle_data,
                        analyze=analyze,
                        start=start,
                        end=end,
                        exchange_name='poloniex',
                        capital_base=10000,
                        base_currency = 'usdt')

More versatile case for minute data

import os
import csv
import pytz
from datetime import datetime

from catalyst.api import record, symbol, symbols
from catalyst.utils.run_algo import run_algorithm


def initialize(context):
# Portfolio assets list
context.asset     = symbol('btc_usdt') # Bitcoin on Poloniex

# Creates a .CSV file with the same name as this script to store results
context.csvfile   = open(os.path.splitext(
                         os.path.basename(__file__))[0]+'.csv', 'w+')
context.csvwriter = csv.writer(context.csvfile)

def handle_data(context, data):
# Variables to record for a given asset: price and volume
# Other options include 'open', 'high', 'open', 'close'
# Please note that 'price' equals 'close'
date   = context.blotter.current_dt     # current time in each iteration
price  = data.current(context.asset, 'price')
volume = data.current(context.asset, 'volume')

# Writes one line to CSV on each iteration with the chosen variables
context.csvwriter.writerow([date,price,volume])

def analyze(context=None, results=None):
# Close open file properly at the end
context.csvfile.close()


# Bitcoin data is available from 2015-3-2. Dates vary for other tokens.
start = datetime(2017, 7, 30, 0, 0, 0, 0, pytz.utc)
end = datetime(2017, 7, 31, 0, 0, 0, 0, pytz.utc)
results = run_algorithm(initialize=initialize,
                        handle_data=handle_data,
                        analyze=analyze,
                        start=start,
                        end=end,
                        exchange_name='poloniex',
                        data_frequency='minute',
                        base_currency ='usdt',
                        capital_base=10000 )