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Robert Carver robcarver17

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View dynamic.py
"""
The starter system has the following features:
- single market
- binary forecast from simple MAV
- exit from trailing stop loss
- fixed positions once in trade
"""
View scratchthis.py
# Gist example of IB wrapper ...
#
# Download API from http://interactivebrokers.github.io/#
#
# Install python API code /IBJts/source/pythonclient $ python3 setup.py install
#
# Note: The test cases, and the documentation refer to a python package called IBApi,
# but the actual package is called ibapi. Go figure.
#
# Get the latest version of the gateway:
View atrvsvol.py
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
robcarver17 / scratch_16.py
Last active Nov 3, 2020
get system risk
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 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 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 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 heuristic correlations.py
### 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):
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