robcarver17

import matplotlib
matplotlib.use("TkAgg")
import matplotlib.pyplot as plt
import scipy.stats as stats
import  numpy as np



def plot_results_for_bin_size(size, pd_result, centre_on_mean = False):
    bins = get_bins_for_size(size, pd_result, centre_on_mean = centre_on_mean)

robcarver17

import pandas as pd
import numpy as np

def get_positions_as_proportion_of_capital(system):

    list_of_instruments = system.get_instrument_list()
    positions = [system.portfolio.get_actual_position(instrument_code) for instrument_code in list_of_instruments]
    positions = pd.concat(positions, axis=1)
    positions.columns = list_of_instruments
    positions[positions.isna()] = 0.0

robcarver17

### Following code is boilerplate for optimising
import matplotlib
matplotlib.use("TkAgg")
import pandas as pd
from scipy.optimize import minimize
import numpy as np
from scipy.stats import norm
from collections import namedtuple

def optimise_for_corr_matrix(corr_matrix):

robcarver17

"""

The starter system has the following features:

- single market
- binary forecast from simple MAV
- exit from trailing stop loss
- fixed positions once in trade

"""

robcarver17

import matplotlib
matplotlib.use("TkAgg")
matplotlib.rcParams.update({'font.size': 22})
from matplotlib.pyplot import hist, plot
import pandas as pd
import numpy as np
from syscore.dateutils import BUSINESS_DAYS_IN_YEAR, ROOT_BDAYS_INYEAR
from syscore.accounting import accountCurveSingleElementOneFreq as accountCurve

def arbitrary_timeindex(Nperiods, index_start=pd.datetime(2000, 1, 1)):

robcarver17

import matplotlib
matplotlib.use("TkAgg")

import matplotlib.pyplot as plt
from scipy.optimize import minimize


import numpy as np
import scipy.stats as stats
from scipy.stats import norm