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View scratch_16.py
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
View scratch_16.py
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)
View get_expected_risk.py
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)
View kings_college.py
#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):
View Create a bunch of trading rules for skew and kurtosis
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
View temp.py
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]
View coinflips.py
import random
from copy import copy
def ishead():
result=random.uniform(0,1)
if result>=0.5:
return 1
else:
return 0
View temp.py
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
View handcrafting.py
"""
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
View handcrafting_test_implementation.py
"""
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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