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big_sweeps.py
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# from logic_functions.bollinger import preprocess_data, logic
# from logic_functions.rsi import preprocess_data, logic
# from logic_functions.rsi_ta import preprocess_data, logic
# from cv2 import threshold, trace
import backtester
import time
import logic_functions.stochastic as stochastic
import logic_functions.rsi as rsi
import logic_functions.bollinger as bollinger
import logic_functions.bb_rsi_longs_only as bb_rsi_longs_only
import logic_functions.rsi_ibs as rsi_ibs
import logic_functions.bb_rsi_stoch as bb_rsi_stoch
import logic_functions.rsi_stochastic as rsi_stochastic
# import logic_functions.bb_rsi_stop_loss
from backtester import tester
import pandas as pd
"""
Modifications to make to the code:
Ensure that logic functions accept v1,v2,v3,v4 as parameters. These will be the values we use to loop over to sweep parameters.
This also changed the backend backtester code
"""
if __name__ == "__main__":
starttime = time.time()
print("time taken: ", time.time() - starttime)
# list_of_stocks = ["TSLA_2020-03-01_2022-01-20_1min"]
list_of_stocks = [
"AAPL_2020-04-18_2022-03-09_1min",
# "AMZN_2020-04-18_2022-03-09_1min",
# "GOOG_2020-04-18_2022-03-09_1min",
"MSFT_2020-04-18_2022-03-09_1min",
# "FB_2020-04-18_2022-03-09_1min",
# "JNJ_2020-04-18_2022-03-09_1min",
"JPM_2020-04-18_2022-03-09_1min",
# "KO_2020-04-18_2022-03-09_1min",
# "LLY_2020-04-18_2022-03-09_1min",
"NVDA_2020-04-18_2022-03-09_1min", # end of 10
# "PEP_2020-04-18_2022-03-09_1min",
# "TSLA_2020-03-01_2022-01-20_1min",
# "UNH_2020-04-18_2022-03-09_1min",
# "V_2020-04-18_2022-03-09_1min",
] # List of stock data csv's to be tested, located in "data/" folder
totalruns = 0
totalnumofruns = 50 + 30 + 120 + 150 + 600 + 120 + 600
# for training_period in range(2, 50, 5): # 10 loops
# print("time taken: ", time.time() - starttime)
# print("Training period: " + str(training_period))
# print(
# "Training period loop number: "
# + str(((training_period - 2) / 5) + 1)
# + "/10"
# )
# for standard_deviation in range(1, 3): # 5 loops
# print("time taken: ", time.time() - starttime)
# totalruns += 1
# print("total runs: " + str(totalruns) + "/" + str(totalnumofruns))
# print("Standard deviation: " + str(standard_deviation) + "/3")
# list_of_stocks_proccessed = (
# bollinger.preprocess_data( # 10 stocks (all 10 at once)
# list_of_stocks, v1=training_period, v2=standard_deviation
# )
# ) # Preprocess the data
# # Bollinger Only
# results = tester.test_array(
# list_of_stocks_proccessed,
# bollinger.logic,
# chart=False,
# v1=training_period,
# v2=standard_deviation,
# ) # Run the backtester
# df = pd.DataFrame(list(results)) # Create dataframe of results
# df.columns = [
# "Buy and Hold",
# "Strategy",
# "Longs",
# "Sells",
# "Shorts",
# "Covers",
# "Trades",
# "Stdev_Strategy",
# "Stdev_Hold",
# "Stock",
# "v1",
# "v2",
# "v3",
# "v4",
# "v5",
# ]
# if training_period == 2 and standard_deviation == 1:
# df.to_csv(
# "results/Test_Results_Bollinger.csv",
# mode="a",
# header=True,
# index=False,
# ) # Save results to csv
# else:
# df.to_csv(
# "results/Test_Results_Bollinger.csv",
# mode="a",
# header=False,
# index=False,
# ) # Save results to csv
# for training_period in range(2, 50, 5): # 10 loops
# print("time taken: ", time.time() - starttime)
# print("Training period: " + str(training_period))
# print(
# "Training period loop number: "
# + str(((training_period - 2) / 5) + 1)
# + "/10"
# )
# for offset in range(-10, 11, 10): # 3 loops
# print("time taken: ", time.time() - starttime)
# totalruns += 1
# print("total runs: " + str(totalruns) + "/" + str(totalnumofruns))
# print("threshold offset: " + str(offset))
# list_of_stocks_proccessed = (
# rsi.preprocess_data( # 10 stocks (all 10 at once)
# list_of_stocks, v1=training_period
# )
# ) # Preprocess the data
# # RSI Only
# results = tester.test_array(
# list_of_stocks_proccessed,
# rsi.logic,
# chart=False,
# v1=training_period,
# v2=80 - offset, # v2 = [90, 80, 70]
# v3=20 + offset, # v3 = [10, 30, 30]
# ) # Run the backtester
# df = pd.DataFrame(list(results)) # Create dataframe of results
# df.columns = [
# "Buy and Hold",
# "Strategy",
# "Longs",
# "Sells",
# "Shorts",
# "Covers",
# "Trades",
# "Stdev_Strategy",
# "Stdev_Hold",
# "Stock",
# "v1",
# "v2",
# "v3",
# "v4",
# "v5",
# ]
# if training_period == 2 and offset == -10:
# df.to_csv(
# "results/Test_Results_RSI.csv",
# mode="a",
# header=True,
# index=False,
# ) # Save results to csv
# else:
# df.to_csv(
# "results/Test_Results_RSI.csv",
# mode="a",
# header=False,
# index=False,
# ) # Save results to csv
# for training_period in range(2, 50, 5): # 10 loops
# print("time taken: ", time.time() - starttime)
# print("Training period: " + str(training_period))
# print(
# "Training period loop number: "
# + str(((training_period - 2) / 5) + 1)
# + "/10"
# )
# for offset in range(-10, 11, 10): # 3 loops
# print("time taken: ", time.time() - starttime)
# print("threshold offset: " + str(offset))
# for rolling_average in [2, 3, 5, 10]: # 4 loops
# if rolling_average < training_period:
# totalruns += 1
# print("rolling average: " + str(rolling_average))
# print("total runs: " + str(totalruns) + "/" + str(totalnumofruns))
# results = tester.test_array(
# list_of_stocks,
# stochastic.logic,
# chart=False,
# v1=training_period,
# v2=80 - offset, # v2 = [90, 80, 70]
# v3=20 + offset, # v3 = [10, 30, 30]
# v4=min(rolling_average, training_period),
# )
# df = pd.DataFrame(list(results)) # Create dataframe of results
# df.columns = [
# "Buy and Hold",
# "Strategy",
# "Longs",
# "Sells",
# "Shorts",
# "Covers",
# "Trades",
# "Stdev_Strategy",
# "Stdev_Hold",
# "Stock",
# "v1",
# "v2",
# "v3",
# "v4",
# "v5",
# ]
# if training_period == 2 and offset == -10 and rolling_average == 2:
# df.to_csv(
# "results/Test_Results_Stochastic.csv",
# mode="a",
# header=True,
# index=False,
# )
# else:
# df.to_csv(
# "results/Test_Results_Stochastic.csv",
# mode="a",
# header=False,
# index=False,
# ) # Save results to csv
# for training_period in range(2, 50, 5): # 10 loops
# print("time taken: ", time.time() - starttime)
# print("Training period: " + str(training_period))
# print(
# "Training period loop number: "
# + str(((training_period - 2) / 5) + 1)
# + "/10"
# )
# for standard_deviation in range(1, 3): # 5 loops
# print("time taken: ", time.time() - starttime)
# print("Standard deviation: " + str(standard_deviation) + "/3")
# for offset in range(-10, 11, 10): # 3 loops
# print("time taken: ", time.time() - starttime)
# totalruns += 1
# print("threshold offset: " + str(offset))
# print("total runs: " + str(totalruns) + "/" + str(totalnumofruns))
# list_of_stocks_proccessed = (
# bb_rsi_longs_only.preprocess_data( # 10 stocks (all 10 at once)
# list_of_stocks, v1=training_period, v2=standard_deviation
# )
# ) # Preprocess the data
# results = tester.test_array(
# list_of_stocks_proccessed,
# bb_rsi_longs_only.logic,
# chart=False,
# v1=training_period,
# v2=standard_deviation,
# v3=80 - offset, # v2 = [90, 80, 70]
# v4=20 + offset, # v3 = [10, 30, 30]
# ) # Run the backtester
# df = pd.DataFrame(list(results)) # Create dataframe of results
# df.columns = [
# "Buy and Hold",
# "Strategy",
# "Longs",
# "Sells",
# "Shorts",
# "Covers",
# "Trades",
# "Stdev_Strategy",
# "Stdev_Hold",
# "Stock",
# "v1",
# "v2",
# "v3",
# "v4",
# "v5",
# ]
# if training_period == 2 and offset == -10 and standard_deviation == 1:
# df.to_csv(
# "results/Test_Results_BB_RSI_Longs.csv",
# mode="a",
# header=True,
# index=False,
# )
# else:
# df.to_csv(
# "results/Test_Results_BB_RSI_Longs.csv",
# mode="a",
# header=False,
# index=False,
# ) # Save results to csv
# for training_period in range(20, 50, 5): # 10 loops fgreggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggg
training_period = 30
standard_deviation = 3
offset = 0
k_period = 20
# for training_period in range(14, 15, 5): # 10 loops
print("time taken: ", time.time() - starttime)
print("Training period: " + str(training_period))
print(
"Training period loop number: " + str(((training_period - 2) / 5) + 1) + "/10"
)
# for standard_deviation in range(3, 4): # 5 loops / 1 loop
# for standard_deviation in range(2, 3, 2): # 5 loops
print("time taken: ", time.time() - starttime)
print("Standard deviation: " + str(standard_deviation) + "/3")
# for offset in range(-10, 11, 10): # 3 loops
# for offset in range(-10, 0, 10):
print("time taken: ", time.time() - starttime)
print("threshold offset: " + str(offset))
print("total runs: " + str(totalruns) + "/" + str(totalnumofruns))
# for k_period in range(8, 21, 6): # 5 loops
# for k_period in [5, 14]:
print("time taken: ", time.time() - starttime)
# if k_period < training_period:
print("k_period: " + str(k_period))
totalruns += 1
list_of_stocks_proccessed = (
bb_rsi_stoch.preprocess_data( # 10 stocks (all 10 at once)
list_of_stocks,
v1=standard_deviation,
v2=training_period,
v3=k_period,
v4=offset,
)
) # Preprocess the data
results = tester.test_array(
list_of_stocks_proccessed,
bb_rsi_stoch.logic,
chart=True,
v1=training_period,
v2=offset,
v3=min(k_period, training_period), # v2 = [90, 80, 70]
v4=standard_deviation
# v4=20 + offset, # v3 = [10, 30, 30]
# v5=min(k_period, training_period),
) # Run the backtester
df = pd.DataFrame(list(results)) # Create dataframe of results
df.columns = [
"Buy and Hold",
"Strategy",
"Longs",
"Sells",
"Shorts",
"Covers",
"Trades",
"Stdev_Strategy",
"Stdev_Hold",
"Stock",
"v1",
"v2",
"v3",
"v4",
"v5",
]
if (
training_period == 2
and offset == -10
and standard_deviation == 1
and k_period == 2
):
df.to_csv(
"results/Test_Results_final_algo.csv",
mode="a",
header=True,
index=False,
)
else:
df.to_csv(
"results/Test_Results_final_algo.csv",
mode="a",
header=False,
index=False,
) # Save results to csv
# for training_period in range(2, 50, 5): # 10 loops
# print("time taken: ", time.time() - starttime)
# print("Training period: " + str(training_period))
# print(
# "Training period loop number: "
# + str(((training_period - 2) / 5) + 1)
# + "/10"
# )
# for offset in range(-10, 11, 10): # 3 loops
# print("time taken: ", time.time() - starttime)
# print("threshold offset: " + str(offset))
# print("total runs: " + str(totalruns) + "/" + str(totalnumofruns))
# for rolling_average in [2, 3, 5, 10]: # 4 loops
# print("time taken: ", time.time() - starttime)
# if rolling_average < training_period:
# print("rolling average: " + str(rolling_average))
# totalruns += 1
# list_of_stocks_proccessed = (
# rsi_stochastic.preprocess_data( # 10 stocks (all 10 at once)
# list_of_stocks, v1=training_period
# )
# ) # Preprocess the data
# results = tester.test_array(
# list_of_stocks_proccessed,
# rsi_stochastic.logic,
# chart=False,
# v1=training_period,
# v2=80 - offset, # v2 = [90, 80, 70]
# v3=20 + offset, # v3 = [10, 30, 30]
# v4=min(rolling_average, training_period),
# ) # Run the backtester
# df = pd.DataFrame(list(results)) # Create dataframe of results
# df.columns = [
# "Buy and Hold",
# "Strategy",
# "Longs",
# "Sells",
# "Shorts",
# "Covers",
# "Trades",
# "Stdev_Strategy",
# "Stdev_Hold",
# "Stock",
# "v1",
# "v2",
# "v3",
# "v4",
# "v5",
# ]
# if training_period == 2 and offset == -10 and rolling_average == 2:
# df.to_csv(
# "results/Test_Results_RSI_Stochastic.csv",
# mode="a",
# header=True,
# index=False,
# )
# else:
# df.to_csv(
# "results/Test_Results_RSI_Stochastic.csv",
# mode="a",
# header=True,
# index=False,
# ) # Save results to csv
# for training_period in range(2, 50, 5): # 10 loops
# print("time taken: ", time.time() - starttime)
# print("Training period: " + str(training_period))
# print(
# "Training period loop number: "
# + str(((training_period - 2) / 5) + 1)
# + "/10"
# )
# for standard_deviation in range(1, 3): # 5 loops
# print("time taken: ", time.time() - starttime)
# print("Standard deviation: " + str(standard_deviation) + "/3")
# for offset in range(-10, 11, 10): # 3 loops
# print("time taken: ", time.time() - starttime)
# print("threshold offset: " + str(offset))
# for stop_loss in [0.05, 0.10, 0.15, 0.20]: # 4 loops
# print("time taken: ", time.time() - starttime)
# if rolling_average < training_period:
# print("rolling average: " + str(rolling_average))
# print(
# "total runs: " + str(totalruns) + "/" + str(totalnumofruns)
# )
# totalruns += 1
# list_of_stocks_proccessed = bb_rsi_stop_loss.preprocess_data( # 10 stocks (all 10 at once)
# list_of_stocks, v1=training_period
# ) # Preprocess the data
# results = tester.test_array(
# list_of_stocks_proccessed,
# bb_rsi_stop_loss.logic,
# chart=False,
# v1=training_period,
# v2=standard_deviation,
# v3=80 - offset, # v2 = [90, 80, 70]
# v4=20 + offset, # v3 = [10, 30, 30]
# v5=min(rolling_average, training_period),
# ) # Run the backtester
# df = pd.DataFrame(list(results)) # Create dataframe of results
# df.columns = [
# "Buy and Hold",
# "Strategy",
# "Longs",
# "Sells",
# "Shorts",
# "Covers",
# "Trades",
# "Stdev_Strategy",
# "Stdev_Hold",
# "Stock",
# "v1",
# "v2",
# "v3",
# "v4",
# "v5",
# ]
# if (
# training_period == 2
# and offset == -10
# and standard_deviation == 1
# and stop_loss == 0.05
# ):
# df.to_csv(
# "results/Test_Results_BB_RSI_Stop_Loss.csv",
# mode="a",
# header=True,
# index=False,
# )
# else:
# df.to_csv(
# "results/Test_Results_BB_RSI_stop_loss.csv",
# mode="a",
# header=True,
# index=False,
# ) # Save results to csv
# for training_period in range(10, 50, 5): # 10 loops
# # for training_period in range(14, 15, 5): # 10 loops
# print("time taken: ", time.time() - starttime)
# print("Training period: " + str(training_period))
# print(
# "Training period loop number: "
# + str(((training_period - 2) / 5) + 1)
# + "/10"
# )
# for standard_deviation in range(2, 4):
# list_of_stocks_proccessed = (
# rsi_ibs.preprocess_data( # 10 stocks (all 10 at once)
# list_of_stocks,
# v1=standard_deviation,
# )
# ) # Preprocess the data
# results = tester.test_array(
# list_of_stocks_proccessed,
# rsi_ibs.logic,
# chart=False,
# v1=training_period,
# ) # Run the backtester
# df = pd.DataFrame(list(results)) # Create dataframe of results
# df.columns = [
# "Buy and Hold",
# "Strategy",
# "Longs",
# "Sells",
# "Shorts",
# "Covers",
# "Trades",
# "Stdev_Strategy",
# "Stdev_Hold",
# "Stock",
# "v1",
# "v2",
# "v3",
# "v4",
# "v5",
# ]
# if (
# training_period == 2
# and offset == -10
# and standard_deviation == 1
# and k_period == 2
# ):
# df.to_csv(
# "results/Test_Results_rsi_ibs.csv",
# mode="a",
# header=True,
# index=False,
# )
# else:
# df.to_csv(
# "results/Test_Results_rsi_ibs.csv",
# mode="a",
# header=False,
# index=False,
# ) # Save results to csv
"""
To test: Bollinger Bands, standard deviation - 10*5 = 50
RSI, Upper + Lower Threshold - 10*3 = 30
Stochastic w/ Average, Upper + Lower Threshold, Rolling average - 10*3*4 = 120
Bollinger + RSI (longs only), standard deviation, Upper + Lower Threshold - 10*5*3 = 150
Bollinger + RSI + Stochastic w/ Average, standard deviation, Upper + Lower Threshold, Rolling average - 10*5*3*4 = 600
Stochastic + RSI Upper + Lower Threshold, Rolling average - 10*3*4 = 120
Bollinger + RSI (longs only) + Stop Losses, standard deviation, Upper + Lower Threshold, Stop Losses - 10*5*3*4 = 600 TEST THIS THEN SEE IF STOPLOSSES ARE GOOD, IF SO THEN ADD TO OTHERS
Bollinger + RSI + Stochastic w/ Average + Stop Losses, standard deviation, Upper + Lower Threshold, Rolling average, Stop losses - 10*5*3*4*6 = 3600
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