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from systems.provided.futures_chapter15.basesystem import *
sys = futures_system()
config = sys.config
del(config.instrument_weights) # ensures all instruments are used equally weighted
config.forecast_weights=dict(ewmac16_64=0.333, ewmac32_128=0.333, ewmac64_256=0.3333)
config.use_forecast_div_mult_estimates=True
config.use_instrument_div_mult_estimates=True
config.use_forecast_scale_estimates=False
system = futures_system(config=config)
acc = system.accounts.portfolio()

robcarver17

from sysdata.quandl.quandl_futures import quandlFuturesConfiguration, quandlFuturesContractPriceData
from sysdata.futures.contracts import listOfFuturesContracts
from sysdata.futures.instruments import futuresInstrument
from sysdata.mongodb.mongo_roll_data import mongoRollParametersData

import numpy as np
import pandas as pd

def get_roll_parameters_from_mongo(instrument_code):

robcarver17

"""
Get some data to test the handcrafting method
"""

from sysdata.csv.csv_sim_futures_data import csvFuturesSimData
from syscore.handcrafting import Portfolio
import pandas as pd

data=csvFuturesSimData()
code_list = ['BOBL', 'BUND', 'US10', 'US20', 'KR3','KR10','EDOLLAR', 'CORN', 'CRUDE_W', 'GAS_US']

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"""
Implement the handcrafting method

This is 'self contained code' which requires wrapping before using in pysystemtrade
"""

## CAVEATS:
## Uses weekly returns (resample needed first)
## Doesn't deal with missing assets

robcarver17

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

from systems.provided.futures_chapter15.estimatedsystem import *
system = futures_system()

del(system.config.instruments) # so we can get results for everything

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import random
from copy import copy

def ishead():
    result=random.uniform(0,1)
    if result>=0.5:
        return 1
    else:
        return 0

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from copy import copy

seq_length = 20
def decimalToBinary(n):
    # return padded binary number as list
    binary_number_as_str=bin(n)
    binary_number_as_str = binary_number_as_str[2:]
    binary_number_as_str = binary_number_as_str.zfill(seq_length)
    binary_number_as_list = [int(x) for x in binary_number_as_str]

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import numpy as np
from systems.provided.futures_chapter15.basesystem import *
import numpy as np
from syscore.algos import robust_vol_calc


## test kurtosis AND skew with trading rules

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#import matplotlib
#matplotlib.use("TkAgg")

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

### Following is the optimisation code:
### First the main function.
def optimise_with_sigma(sigma, mean_list):

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import pandas as pd
import  numpy as np


def get_expected_risk_for_system(system):

    value_of_positions_proportion_capital = get_positions_as_proportion_of_capital(system)

    instrument_returns = get_instrument_returns(system)
    instrument_returns = instrument_returns.ffill().reindex(value_of_positions_proportion_capital.index)