mirror of
https://github.com/wassname/Volt.git
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cleanup
This commit is contained in:
wjmaddox
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*/.ipynb_checkpoints/*
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*/__pycache__/*
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Vendored
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import numpy as np
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import torch
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import pandas as pd
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import gpytorch
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import argparse
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import datetime
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import warnings
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from voltron.data import make_ticker_list, GetStockHistory
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import sys
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from GenerateMultiMeanPreds import GenerateStockPredictions, GenerateBasicPredictions
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from gpytorch.utils.warnings import NumericalWarning
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warnings.simplefilter("ignore", NumericalWarning)
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def main(args):
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ticker_file = args.ticker_fname + ".txt"
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tckr_list = make_ticker_list(ticker_file)
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if args.end_date.lower() == "none":
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end_date = datetime.date.today()
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else:
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end_date = datetime.datetime.strptime(args.end_date, "%Y-%m-%d")
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for tckr in tckr_list:
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# try:
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data = GetStockHistory(tckr, history=args.ntrain + args.lookback)
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if args.kernel.lower() == 'volt':
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GenerateStockPredictions(tckr, data, forecast_horizon=args.forecast_horizon,
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train_iters=args.train_iters,
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nsample=args.nsample, mean=args.mean,
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ntrain=args.ntrain, save=args.save,
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ntimes=args.ntimes)
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else:
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GenerateBasicPredictions(tckr, data, forecast_horizon=args.forecast_horizon,
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kernel_name=args.kernel, mean_name=args.mean,
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k=args.k, train_iters=args.train_iters,
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nsample=args.nsample, ntimes=args.ntimes,
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ntrain=args.ntrain, save=args.save)
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# except:
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# print("FAILED ", tckr)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--ticker_fname",
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type=str,
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default='test_tickers',
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)
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parser.add_argument(
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"--ntrain",
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type=int,
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default=400,
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)
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parser.add_argument(
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"--ntimes",
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type=int,
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default=25,
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)
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parser.add_argument(
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"--forecast_horizon",
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type=int,
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default=100,
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)
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parser.add_argument(
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'--kernel',
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type=str,
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default="volt",
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)
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parser.add_argument(
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'--mean',
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type=str,
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default="ewma",
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)
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parser.add_argument(
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"--nsample",
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type=int,
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default=1000,
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)
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parser.add_argument(
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"--printing",
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type=bool,
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default=False
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)
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parser.add_argument(
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"--train_iters",
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type=int,
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default=300,
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)
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parser.add_argument(
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"--end_date",
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default="none",
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)
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parser.add_argument(
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"--lookback",
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type=int,
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default=500,
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)
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parser.add_argument(
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"--save",
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type=bool,
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default=True,
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)
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parser.add_argument(
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"--k",
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type=int,
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default=100,
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)
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args = parser.parse_args()
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main(args)
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@@ -1,298 +0,0 @@
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import matplotlib.pyplot as plt
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import seaborn as sns
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import numpy as np
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import torch
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import pandas as pd
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import os
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import gpytorch
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import argparse
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import datetime
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from botorch.models import SingleTaskGP
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from botorch.optim.fit import fit_gpytorch_torch
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from gpytorch.likelihoods import GaussianLikelihood
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from gpytorch.mlls import ExactMarginalLogLikelihood
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from gpytorch.means import ConstantMean, LinearMean
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from gpytorch.kernels import SpectralMixtureKernel, MaternKernel, RBFKernel, ScaleKernel
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from voltron.means import EWMAMean, DEWMAMean, TEWMAMean
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from voltron.train_utils import LearnGPCV, TrainVolModel, TrainVoltMagpieModel, TrainBasicModel
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from voltron.models import VoltMagpie
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from voltron.means import LogLinearMean
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from voltron.rollout_utils import GeneratePrediction, Rollouts
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from voltron.data import make_ticker_list, DataGetter, GetStockHistory
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def GenerateGPCVPredictions(ticker, dat,
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forecast_horizon=20, ntimes=25,
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train_iters=400, nsample=1000,
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ntrain=400):
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end_idxs = torch.arange(ntrain, dat.shape[0],
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int((dat.shape[0]-ntrain)/ntimes))
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ntest = forecast_horizon
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dt = 1./252
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savepath = "./saved-outputs/" + ticker + "/"
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if not os.path.exists(savepath):
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os.mkdir(savepath)
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for last_day in end_idxs:
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date = str(dat.index[last_day.item()].date())
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print(date, ticker)
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train_y = torch.FloatTensor(dat.Close[last_day.item()-ntrain:last_day.item()].to_numpy())
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train_x = torch.arange(train_y.shape[0]-1) * dt
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test_x = torch.arange(ntest) * dt + train_x[-1] + train_x[1]
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# try:
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use_cuda = torch.cuda.is_available()
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if use_cuda:
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train_x = train_x.cuda()
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test_x = test_x.cuda()
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train_y = train_y.cuda()
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model, likelihood = LearnGPCV(train_x, train_y,
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train_iters=train_iters, printing=False, return_model=True)
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preds = likelihood(model(test_x),
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return_gaussian=False).sample(torch.Size((nsample,)))
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preds = preds.cumsum(-1).squeeze()
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preds = preds.view(-1, preds.shape[-1])
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save_samples = preds.view(-1, preds.shape[-1]) * (dt ** 0.5) + train_y[-1].log()
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torch.save(save_samples, savepath + "gpcv_" + date + ".pt")
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return
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def GenerateStockPredictions(ticker, dat,
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forecast_horizon=20,
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train_iters=400, nsample=1000,
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ntrain=400, mean='ewma', kernel='volt',
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save=False, k=300, ntimes=-1):
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if ntimes == -1:
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end_idxs = torch.arange(ntrain, dat.shape[0])
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else:
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end_idxs = torch.arange(ntrain, dat.shape[0],
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int((dat.shape[0]-ntrain)/ntimes))
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ntest = forecast_horizon
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dt = 1./252
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model_name = kernel + "_" + mean + str(k) + "_"
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par_dir = "./saved-outputs/"
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if not os.path.exists(par_dir):
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os.mkdir(par_dir)
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savepath = par_dir + ticker + "/"
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if not os.path.exists(savepath):
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os.mkdir(savepath)
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for last_day in end_idxs:
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date = str(dat.index[last_day.item()].date())
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# try:
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train_y = torch.FloatTensor(dat.Close[last_day.item()-ntrain:last_day.item()].to_numpy())
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train_x = torch.arange(train_y.shape[0]-1) * dt
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test_x = torch.arange(ntest) * dt + train_x[-1] + train_x[1]
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# try:
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use_cuda = torch.cuda.is_available()
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if use_cuda:
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train_x = train_x.cuda()
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test_x = test_x.cuda()
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train_y = train_y.cuda()
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# print("Producing " + ticker + " Forecasts.....")
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if kernel == "volt":
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vol = LearnGPCV(train_x, train_y, train_iters=train_iters,
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printing=False)
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vmod, vlh = TrainVolModel(train_x, vol,
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train_iters=train_iters, printing=False)
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voltron, lh = TrainVoltMagpieModel(train_x, train_y[1:],
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vmod, vlh, vol,
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printing=False,
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train_iters=train_iters,
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k=k, mean_func=mean)
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vmod.eval();
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if mean in ['ewma', 'dewma', 'tewma']:
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save_samples = Rollouts(train_x, train_y, test_x, voltron,
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nsample=nsample)
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else: ## VOLT + STANDARD MEAN
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voltron.vol_model.eval()
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predvol = voltron.vol_model(test_x).sample(torch.Size((nsample, ))).exp()
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save_samples = GeneratePrediction(train_x, train_y, test_x,
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predvol, voltron).detach()
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del predvol
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del voltron, lh, vmod, vlh, vol
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torch.cuda.empty_cache()
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if save:
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if not os.path.exists(savepath):
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os.mkdir(savepath)
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torch.save(save_samples, savepath + model_name + date + ".pt")
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# except:
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# nans = torch.ones(nsample, ntest) * torch.nan
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# if save:
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# if not os.path.exists(savepath):
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# os.mkdir(savepath)
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# torch.save(nans, savepath + model_name + date + ".pt")
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return dat, save_samples
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def GenerateOneDayPredictions(ticker, train_y, date,
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forecast_horizon=20,
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train_iters=400, nsample=1000,
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ntrain=400, save=False, mean=None):
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ntest = forecast_horizon
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dt = 1./252
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par_dir = "./saved-outputs/"
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if not os.path.exists(par_dir):
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os.mkdir(par_dir)
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savepath = par_dir + ticker + "/"
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if not os.path.exists(savepath):
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os.mkdir(savepath)
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train_x = torch.arange(train_y.shape[0]-1) * dt
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test_x = torch.arange(ntest) * dt + train_x[-1] + train_x[1]
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use_cuda = torch.cuda.is_available()
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if use_cuda:
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train_x = train_x.cuda()
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test_x = test_x.cuda()
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train_y = train_y.cuda()
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vol = LearnGPCV(train_x, train_y, train_iters=train_iters,
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printing=False)
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vmod, vlh = TrainVolModel(train_x, vol,
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train_iters=train_iters, printing=False)
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if mean=='constant':
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voltron, lh = TrainVoltMagpieModel(train_x, train_y[1:],
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vmod, vlh, vol,
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printing=False,
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train_iters=200,
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mean_func='constant')
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vmod.eval();
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voltron.eval();
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save_samples = Rollouts(train_x, train_y, test_x, voltron,
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nsample=nsample)
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if save:
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model_name = "volt_" + mean + "_"
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torch.save(save_samples, savepath + model_name + date + ".pt")
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else:
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for mean in ['ewma', 'dewma', 'tewma']:
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for k in [25, 50, 100, 200, 300, 400]:
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try:
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voltron, lh = TrainVoltMagpieModel(train_x, train_y[1:],
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vmod, vlh, vol,
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printing=False,
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train_iters=0,
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k=k, mean_func=mean)
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vmod.eval();
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voltron.eval();
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save_samples = Rollouts(train_x, train_y, test_x, voltron,
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nsample=nsample)
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except:
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print("Failed: ", ticker, mean, k)
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if save:
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save_samples = torch.ones(nsample, ntest) * torch.nan
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if save:
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model_name = "volt_" + mean + str(k) + "_"
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torch.save(save_samples, savepath + model_name + date + ".pt")
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del voltron, lh, vmod, vlh, vol, save_samples
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torch.cuda.empty_cache()
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return
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def GenerateBasicPredictions(ticker, dat, kernel_name, mean_name='ewma', k=400,
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forecast_horizon=100,
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train_iters=600, nsample=1000,
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ntrain=400, save=False, ntimes=-1):
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if ntimes == -1:
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end_idxs = torch.arange(ntrain, dat.shape[0])
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else:
|
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end_idxs = torch.arange(ntrain, dat.shape[0],
|
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int((dat.shape[0]-ntrain)/ntimes))
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ntest = forecast_horizon
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dt = 1./252
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par_dir = "./saved-outputs/"
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if not os.path.exists(par_dir):
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os.mkdir(par_dir)
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savepath = par_dir + ticker + "/"
|
||||
if not os.path.exists(savepath):
|
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os.mkdir(savepath)
|
||||
|
||||
for last_day in end_idxs:
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date = str(dat.index[last_day.item()].date())
|
||||
train_y = torch.FloatTensor(dat.Close[last_day.item()-ntrain:last_day.item()].to_numpy())
|
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train_x = torch.arange(train_y.shape[0]-1) * dt
|
||||
test_x = torch.arange(ntest) * dt + train_x[-1] + train_x[1]
|
||||
# try:
|
||||
use_cuda = torch.cuda.is_available()
|
||||
if use_cuda:
|
||||
train_x = train_x.cuda()
|
||||
test_x = test_x.cuda()
|
||||
train_y = train_y.cuda()
|
||||
|
||||
# print("Producing " + ticker + " Forecasts.....")
|
||||
kernel_possibilities = {"sm": SpectralMixtureKernel,
|
||||
"matern": MaternKernel,
|
||||
"rbf": RBFKernel}
|
||||
kernel = kernel_possibilities[kernel_name.lower()]
|
||||
if kernel_name.lower() != 'sm':
|
||||
kernel = ScaleKernel(kernel())
|
||||
else:
|
||||
kernel = kernel(num_mixtures=15)
|
||||
kernel.initialize_from_data_empspect(train_x, train_y.log())
|
||||
|
||||
train_y = train_y[1:]
|
||||
|
||||
model = SingleTaskGP(
|
||||
train_x.view(-1,1),
|
||||
train_y.log().reshape(-1, 1),
|
||||
covar_module=kernel,
|
||||
likelihood=GaussianLikelihood()
|
||||
)
|
||||
|
||||
mean_name = mean_name.lower()
|
||||
if mean_name == "loglinear":
|
||||
model.mean_module = LogLinearMean(1)
|
||||
model.mean_module.initialize_from_data(train_x, train_y.log())
|
||||
elif mean_name == 'linear':
|
||||
model.mean_module = LinearMean(1)
|
||||
elif mean_name == "constant":
|
||||
model.mean_module = ConstantMean()
|
||||
elif mean_name == "ewma":
|
||||
model.mean_module = EWMAMean(train_x, train_y.log(), k=k).to(train_x.device)
|
||||
elif mean_name == "dewma":
|
||||
model.mean_module = DEWMAMean(train_x, train_y.log(), k=k).to(train_x.device)
|
||||
elif mean_name == "tewma":
|
||||
model.mean_module = TEWMAMean(train_x, train_y.log(), k=k).to(train_x.device)
|
||||
|
||||
if use_cuda:
|
||||
model = model.to(train_x.device)
|
||||
print("Fitting Model", ticker)
|
||||
mll = ExactMarginalLogLikelihood(model.likelihood, model)
|
||||
fit_gpytorch_torch(mll, options={'maxiter':train_iters, 'disp':False})
|
||||
|
||||
if mean_name in ["loglinear", "constant", 'linear']:
|
||||
save_samples = model.posterior(test_x).sample(torch.Size((nsample,
|
||||
))).squeeze(-1).cpu().detach()
|
||||
else:
|
||||
save_samples = Rollouts(
|
||||
train_x, train_y, test_x, model, nsample=nsample, method = "nonvol"
|
||||
).cpu().detach()
|
||||
|
||||
model_name = kernel_name + "_" + mean_name + str(k) + "_"
|
||||
torch.save(save_samples, savepath + model_name + date + ".pt")
|
||||
|
||||
model.train()
|
||||
torch.cuda.empty_cache()
|
||||
del model
|
||||
|
||||
|
||||
return dat, save_samples
|
||||
@@ -1,133 +0,0 @@
|
||||
import numpy as np
|
||||
import torch
|
||||
import pandas as pd
|
||||
import gpytorch
|
||||
import argparse
|
||||
import datetime
|
||||
import warnings
|
||||
import os
|
||||
from voltron.data import make_ticker_list, GetStockHistory
|
||||
from LSTMUtils import SequenceDataset, LSTM, TrainLSTM, LSTMRollouts, NLL
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
def main(args):
|
||||
|
||||
data_path = "../../voltron/data/"
|
||||
ticker_file = args.ticker_fname + ".txt"
|
||||
tckr_list = make_ticker_list(data_path + ticker_file)
|
||||
# tckr_list = ['TSLA']
|
||||
|
||||
use_cuda = False
|
||||
if torch.cuda.is_available():
|
||||
use_cuda = True
|
||||
|
||||
ntest = args.forecast_horizon
|
||||
ntrain = args.ntrain
|
||||
seq_len = args.seq_length
|
||||
|
||||
if args.end_date.lower() == "none":
|
||||
end_date = datetime.date.today()
|
||||
else:
|
||||
end_date = datetime.datetime.strptime(args.end_date, "%Y-%m-%d")
|
||||
|
||||
|
||||
for tckr in tckr_list:
|
||||
try:
|
||||
data = GetStockHistory(tckr, history= ntrain + args.lookback)
|
||||
end_idxs = torch.arange(args.ntrain, data.shape[0],
|
||||
int((data.shape[0]-args.ntrain)/args.ntimes))
|
||||
|
||||
savepath = "./saved-outputs/" + tckr + "/"
|
||||
if not os.path.exists(savepath):
|
||||
os.mkdir(savepath)
|
||||
|
||||
for last_day in end_idxs:
|
||||
date = str(data.index[last_day.item()].date())
|
||||
raw_y = data.Close[last_day.item()-ntrain:last_day.item()].to_numpy()
|
||||
raw_y = torch.FloatTensor(raw_y).log()
|
||||
train_y = (raw_y - raw_y.mean())/raw_y.std()
|
||||
|
||||
## make trainloader ##
|
||||
dset = SequenceDataset(train_y, seq_len)
|
||||
trainloader = DataLoader(dset, batch_size=args.batch_size, shuffle=True)
|
||||
|
||||
model = LSTM(2, seq_len, 128, 1)
|
||||
if use_cuda:
|
||||
model = model.cuda()
|
||||
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
|
||||
TrainLSTM(trainloader, model, NLL, optimizer, epochs=args.train_epochs,
|
||||
printing=True, use_cuda=use_cuda)
|
||||
|
||||
rollouts = LSTMRollouts(model, args.nsample, ntest,
|
||||
dset, use_cuda).cpu()
|
||||
rollouts = rollouts * raw_y.std() + raw_y.mean()
|
||||
torch.save(rollouts, savepath + "lstm_" + date + ".pt")
|
||||
|
||||
del model
|
||||
except:
|
||||
print("FAILED ", tckr)
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"--ntimes",
|
||||
type=int,
|
||||
default=25,
|
||||
)
|
||||
parser.add_argument(
|
||||
"--forecast_horizon",
|
||||
type=int,
|
||||
default=20,
|
||||
)
|
||||
parser.add_argument(
|
||||
"--seq_length",
|
||||
type=int,
|
||||
default=25,
|
||||
)
|
||||
parser.add_argument(
|
||||
"--ticker_fname",
|
||||
type=str,
|
||||
default='test_tickers',
|
||||
)
|
||||
parser.add_argument(
|
||||
"--ntrain",
|
||||
type=int,
|
||||
default=400,
|
||||
)
|
||||
parser.add_argument(
|
||||
"--batch_size",
|
||||
type=int,
|
||||
default=128,
|
||||
)
|
||||
parser.add_argument(
|
||||
"--nsample",
|
||||
type=int,
|
||||
default=1000,
|
||||
)
|
||||
parser.add_argument(
|
||||
"--printing",
|
||||
type=bool,
|
||||
default=False
|
||||
)
|
||||
parser.add_argument(
|
||||
"--train_epochs",
|
||||
type=int,
|
||||
default=200,
|
||||
)
|
||||
parser.add_argument(
|
||||
"--end_date",
|
||||
default="none",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--lookback",
|
||||
type=int,
|
||||
default=500,
|
||||
)
|
||||
parser.add_argument(
|
||||
"--save",
|
||||
type=bool,
|
||||
default=False,
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
main(args)
|
||||
@@ -1,3 +0,0 @@
|
||||
AAPL
|
||||
MSFT
|
||||
GOOG
|
||||
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Vendored
BIN
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Reference in New Issue
Block a user