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import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pyrenko
import scipy.optimize as opt
from scipy.stats import iqr
import talib

from catalyst import run_algorithm
from catalyst.api import (record, symbol, order_target, order_target_percent, get_datetime)

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def initialize(context):
    context.asset = symbol('eth_btc')

    context.leverage = 1.0              # 1.0 - no leverage
    context.n_history = 24 * 15         # Number of lookback bars for modelling
    context.tf = '60T'                  # How many minutes in a timeframe
    context.model = pyrenko.renko()     # Renko object
    context.part_cover_ratio = 0.166    # Partially cover position ratio
    context.last_brick_size = 0.0       # Last optimal brick size (just for storing)
    

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# Function for optimization
def evaluate_renko(brick, history, column_name):
    renko_obj = pyrenko.renko()
    renko_obj.set_brick_size(brick_size = brick, auto = False)
    renko_obj.build_history(prices = history)
    return renko_obj.evaluate()[column_name]

def handle_data(context, data):
    current_time = get_datetime().time()
    if current_time.hour == 0 and current_time.minute == 0:

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def analyze(context, perf):
    # Summary output
    print('Total return: ' + str(perf.algorithm_period_return[-1]))
    print('Sortino coef: ' + str(perf.sortino[-1]))
    print('Max drawdown: ' + str(np.min(perf.max_drawdown)))
    print('Alpha: ' + str(perf.alpha[-1]))
    print('Beta: ' + str(perf.beta[-1]))
    perf.to_csv('perf_' + str(context.asset) + '.csv')
    
    f = plt.figure(figsize = (7.2, 7.2))

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run_algorithm(
    capital_base = 10,
    data_frequency = 'daily',
    initialize = initialize,
    handle_data = handle_data,
    analyze = analyze,
    exchange_name = 'bitfinex',
    quote_currency = 'btc',
    start = pd.to_datetime('2017-12-1', utc = True),
    end = pd.to_datetime('2018-11-12', utc = True))

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python renko_trend_following.py

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import quandl
import pandas as pd
from matplotlib import pyplot as plt
import requests
import statsmodels.tsa.stattools as ts 
from statsmodels.tsa.vector_ar.vecm import coint_johansen

def get_bitfinex_asset(asset, ts_ms_start, ts_ms_end):
    url = 'https://api.bitfinex.com/v2/candles/trade:1D:t' + asset + '/hist'
    params = { 'start': ts_ms_start, 'end': ts_ms_end, 'sort': 1}

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BTCUSD and ETHUSD: p-value = 0.06576979804268955
BTCUSD and LTCUSD: p-value = 0.07347140678450967
BTCUSD and XMRUSD: p-value = 0.021570889424181703
BTCUSD and NEOUSD: p-value = 0.10239483419041967
BTCUSD and XRPUSD: p-value = 0.00900122457399106
BTCUSD and ZECUSD: p-value = 0.16378128244807538
ETHUSD and BTCUSD: p-value = 0.31796015423321283
ETHUSD and LTCUSD: p-value = 0.609075825185015
ETHUSD and XMRUSD: p-value = 0.17284643088428048
ETHUSD and NEOUSD: p-value = 0.12876967722061067

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import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.stats as st

from catalyst import run_algorithm
from catalyst.api import (record, symbol, order_target_percent, date_rules, time_rules, get_datetime)

def initialize(context):
    context.A = symbol('xmr_usd')

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context.set_commission(maker = 0.001, taker = 0.002)
context.set_slippage(slippage = 0.0005)