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def show_worst_drawdown_periods(returns, top=5):
    """
    Prints information about the worst drawdown periods.

    Prints peak dates, valley dates, recovery dates, and net
    drawdowns.

    Parameters
    ----------
    returns : pd.Series

lamres

import backtrader as bt
import pandas as pd
import numpy as np


class PivotPointLine(bt.Indicator):
    lines = ('pivot_up', 'pivot_down', 'pl_value', 'direction',)
    params = (
        ('pivot_window_len', 12),
        ('history_bars_as_multiple_pwl', 30)

lamres

import backtrader as bt
from PivotPointLineIndicator import PivotPointLine

# Create a Strategy
class TrendBreakerPL(bt.Strategy):
    params = (
        ('pivot_window_len', 12),
        ('history_bars_as_multiple_pwl', 30),
        ('fixed_tp', 0.08),
        ('fixed_sl_as_multiple_tp', 0.15),

lamres

import backtrader.feeds as btfeed


class FinamHLOC(btfeed.GenericCSVData):
    params = (
        ('dtformat', ('%Y%m%d')),
        ('tmformat', ('%H%M%S')),
        ('datetime', 2),
        ('time', 3),
        ('high', 5),

lamres

import backtrader as bt
import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
from DataFeedFormat import FinamHLOC
from TrendBreakerPLStrategy import TrendBreakerPL
import pyfolio as pf
import seaborn as sns
sns.set_style("whitegrid")

lamres

from BacktestTrendBreakerPL import BacktestTrendBreakerPL
import backtrader as bt
import warnings
warnings.filterwarnings("ignore")

from pyswarm import pso

# The objective function for optimization
def obj_fun(x):
    print('')

lamres

run_algorithm(
    capital_base = 100000,
    data_frequency = 'minute',
    initialize = initialize,
    handle_data = handle_data,
    analyze = analyze,
    exchange_name = 'bitfinex',
    quote_currency = 'usd',
    start = pd.to_datetime('2018-1-1', utc = True),
    end = pd.to_datetime('2019-5-22', utc = True))

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def analyze(context, perf):
    sns.set()

    # 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[-1])))
    print("alpha: " + str(perf.alpha[-1]))
    print("beta: " + str(perf.beta[-1]))

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def handle_data(context, data):
    current_date = get_datetime().date()
    current_time = get_datetime().time()

    # Just one time in a day (first minute)
    if current_time.hour == 0 and current_time.minute == 0 and current_time.second == 0:
        prices = pd.DataFrame()
        volumes = pd.DataFrame()

        try:

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def initialize(context):
    context.asset = symbol('btc_usd')
    context.leverage = 1.0

    context.std_period = 10
    context.ma_period = 10
    context.price_deviation_period = 10
    context.volume_deviation_period = 10
    context.n_periods = 5 + int(np.max([context.std_period, context.ma_period,
        context.price_deviation_period, context.volume_deviation_period]))