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analysis60min.py
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analysis60min.py
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# Author: Fabio Rodrigues Pereira
# E-mail: fabior@uio.no
from helper import plotReturnTrajectories, plotMeanReturnTrajectory
from helper import run500times, optimal500, plotDist, plotBox
import pandas as pd
import numpy as np
import os
ROOT_DIR = os.path.abspath(os.curdir)
files = [
f"{ROOT_DIR}/data/WINJ21/WINJ21_60min.json",
f"{ROOT_DIR}/data/WINM21/WINM21_60min.json",
f"{ROOT_DIR}/data/WINQ21/WINQ21_60min.json",
f"{ROOT_DIR}/data/WINV21/WINV21_60min.json",
f"{ROOT_DIR}/data/WINZ21/WINZ21_60min.json",
f"{ROOT_DIR}/data/WING22/WING22_60min.json"
]
# #################### Discussion
params = ["GreedyGQ", 5, "sigmoid", "minusMean", 0.01, 0.95, 0.1, 200]
objectsGreedyGQ = run500times(params, files)
optimalGreedyGQ = optimal500(objectsGreedyGQ)
plotReturnTrajectories(
optimal=optimalGreedyGQ,
initInvest=28000,
algoType=params[0],
showPlot=True,
timeFramed="60 min"
)
plotMeanReturnTrajectory(
optimal=optimalGreedyGQ,
initInvest=28000,
algoType=params[0],
showPlot=True,
timeFramed="60 min"
)
plotDist(
optimal=optimalGreedyGQ,
initInvest=28000,
algoType=params[0],
showPlot=True,
timeFramed="60 min"
)
plotBox(
optimal=optimalGreedyGQ,
initInvest=28000,
algoType=params[0],
showPlot=True,
timeFramed="60 min"
)
params = ["QLearn", 5, "sigmoid", "minusMean", 0.01, 0.95, 0]
objectsQLearn = run500times(params, files)
optimalQLearn = optimal500(objectsQLearn)
plotReturnTrajectories(
optimal=optimalQLearn,
initInvest=28000,
algoType=params[0],
showPlot=True,
timeFramed="60 min"
)
plotMeanReturnTrajectory(
optimal=optimalQLearn,
initInvest=28000,
algoType=params[0],
showPlot=True,
timeFramed="60 min"
)
plotDist(
optimal=optimalQLearn,
initInvest=28000,
algoType=params[0],
showPlot=True,
timeFramed="60 min"
)
plotBox(
optimal=optimalQLearn,
initInvest=28000,
algoType=params[0],
showPlot=True,
timeFramed="60 min"
)
params = ["SARSA", 5, "hypTanh", "minusMean", 0.01, 1, 0.1, "zeros", 200]
objectsSARSA = run500times(params, files)
optimalSARSA = optimal500(objectsSARSA)
plotReturnTrajectories(
optimal=optimalSARSA,
initInvest=28000,
algoType=params[0],
showPlot=True,
timeFramed="60--min"
)
plotMeanReturnTrajectory(
optimal=optimalSARSA,
initInvest=28000,
algoType=params[0],
showPlot=True,
timeFramed="60--min"
)
plotDist(
optimal=optimalSARSA,
initInvest=28000,
algoType=params[0],
showPlot=True,
timeFramed="60--min"
)
plotBox(
optimal=optimalSARSA,
initInvest=28000,
algoType=params[0],
showPlot=True,
timeFramed="60--min"
)
# ########## saving for combined analysis
optimals = np.hstack(
[
optimalGreedyGQ["histRprime"][:, -1].reshape(-1, 1),
optimalQLearn["histRprime"][:, -1].reshape(-1, 1),
optimalSARSA["histRprime"][:, -1].reshape(-1, 1)
]
)
optimals = pd.DataFrame(
optimals,
columns=["Greedy60--min", "QLearn60--min", "SARSA60--min"]
)
optimals.to_csv(
f"{ROOT_DIR}/results/optimals60--min.csv",
index_label=True
)