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Repository: kayuksel/forex-rl-challenge
Branch: master
Commit: 116a68a0da73
Files: 17
Total size: 17.4 MB
Directory structure:
gitextract_50dtzs9b/
├── .gitignore
├── LICENSE
├── README.md
├── adamw.py
├── autoencoder.py
├── dataset/
│ ├── X_test.pkl
│ ├── y_test.pkl
│ └── y_train.pkl
├── example_cpu.py
├── example_gpu.py
├── models/
│ ├── model.pt
│ ├── policy_weights.npy
│ └── weights.pkl
├── outputs/
│ ├── hourly_returns.csv
│ └── portfolio_weights.csv
├── requirements.txt
└── shared.py
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FILE CONTENTS
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FILE: .gitignore
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dataset/X_train.pkl
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FILE: LICENSE
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You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.
================================================
FILE: README.md
================================================
# FX Reinforcement Learning Playground
This repository contains an open challenge for a Portfolio Balancing AI in Forex.
The state of the FX market is represented via 512 features in X_train and X_test.
These 512 features summarizes the price-actions of 10+1 assets in past 10 days.
Hourly log returns of assets during train & test periods are in y_train and y_test.
Why use my features as environment summary? because they're performing well!
# Participation into the Forex RL Challenge
**example.py** contains an implementation, which balances a long-short FX portfolio.
Up to 2x leverage is allowed. Your objective is to outperform following risk metrics.
Please send me your saved model so that I can test it on a blind set for the contest.
I will list results from challengers here by sorting them using the industry-standard
risk measures including (but not limited to) the Calmar, Sortino, Omega ratio(s), etc.
# Test results of example_cpu.py available here:
Max. Drawdown: 7.74%
Sortino Ratio: 9.30x
Sharpe Ratio: 3.35x
Stability: 90.31%
Tail Ratio: 3.67x
Value at Risk: -0.94%
# Daily Portfolio Balances
# Annual Cumulative Return
# Weekly Portfolio Log Return
# How you can get rich out of this?
If you obtain successful results and you want to use your RL model for live-trading,
you can contact me for subscribing to a real-time feed of the environment summary.
Your objective results here can also attract business opportunities such as job offers.
# If you are interested in academics:
Are you an academician? Use FX RL challenge to experiment with it in your publication.
You won't need to worry about the right feature extraction, as I already did that for you.
Fixed input for challengers will allow a clear benchmarking of RL methods developed.
Below is a survey paper about reinforcement learning applications in financial markets.
https://www.statistik.rw.fau.de/files/2018/10/12-2018.pdf
# Sponsorships and Hackhathons
Please, contact me if you would like to sponsor the FX RL challenge; or organize a local
meetup, workshop or Hackathon where RL practitioners can participate to this challenge.
# Bonus: Example Implementation
PyTorch implementation of Multi-processed training of a shared model where batches
are divided across processes. Looking for a person to contribute a TensorFlow version.
Multi-threaded implementation of Augmented Random Search method is also available!
Run the GPU version and then post-train with the CPU version (ARS) to get best results.
# Bonus: Autoencoder Interpretation
PyTorch implementation of Variational Autoencoder (VAE) provided in the repository
demonstrates that features has great ability of capturing the right direction for the
majority of the extreme movements of the AUD (left) and NZD (right) assets, which
is the key to the success of the portfolio balancing policy that has been converged.
================================================
FILE: adamw.py
================================================
import math
import torch
from torch.optim.optimizer import Optimizer
class AdamW(Optimizer):
def __init__(self, params, lr=1e-4, betas=(0.9, 0.999), eps=1e-5, weight_decay=1e-5, hypergrad=0, partial=1):
if not 0.0 <= betas[0] < 1.0:
raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
if not 0.0 <= betas[1] < 1.0:
raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, hypergrad=hypergrad, partial=partial)
super().__init__(params, defaults)
def step(self, closure=None):
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.data
if grad.is_sparse:
raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
state = self.state[p]
if len(state) == 0:
state['step'] = 0
state['exp_avg'] = torch.zeros_like(p.data)
state['exp_avg_sq'] = torch.zeros_like(p.data)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
beta1, beta2 = group['betas']
state['step'] += 1
if group['hypergrad'] > 0 and state['step'] > 1:
prev_bias_correction1 = 1 - beta1 ** (state['step'] - 1)
prev_bias_correction2 = 1 - beta2 ** (state['step'] - 1)
h = torch.dot(grad.view(-1), torch.div(exp_avg, exp_avg_sq.sqrt().add_(group['eps'])).view(-1)) * math.sqrt(prev_bias_correction2) / prev_bias_correction1
group['lr'] += group['hypergrad'] * h
exp_avg.mul_(beta1).add_(1 - beta1, grad)
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
denom = exp_avg_sq.sqrt().add_(group['eps'])
bias_correction1 = 1 - beta1 ** state['step']
bias_correction2 = 1 - beta2 ** state['step']
step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
if group['weight_decay'] != 0:
decayed_weights = torch.mul(p.data, group['weight_decay'])
p.data.addcdiv_(-step_size, exp_avg, denom**group['partial'])
p.data.sub_(decayed_weights)
else:
p.data.addcdiv_(-step_size, exp_avg, denom**group['partial'])
return loss
================================================
FILE: autoencoder.py
================================================
import matplotlib
matplotlib.use('Agg')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from torchvision import datasets
import os, time, pdb, math, random
import _pickle as cPickle
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
torch.manual_seed(0)
torch.cuda.manual_seed(0)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
root = '/home/kamer/Desktop/fx/'
with open(root+'dataset/y_train.pkl', 'rb') as f: y_train = cPickle.load(f)
with open(root+'dataset/y_test.pkl', 'rb') as f: y_test = cPickle.load(f)
with open(root+'dataset/X_train.pkl', 'rb') as f: X_train = cPickle.load(f)
with open(root+'dataset/X_test.pkl', 'rb') as f: X_test = cPickle.load(f)
train_dataset = torch.utils.data.TensorDataset(X_train)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size = len(train_dataset), shuffle = True)
aud = y_test[:, 0]
aud -= np.mean(aud)
nzd = y_test[:, 3]
nzd -= np.mean(nzd)
class GELU(nn.Module):
def forward(self, x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
class VAE(nn.Module):
def __init__(self):
super(VAE, self).__init__()
self.fc0 = nn.Linear(512, 256)
self.fc1 = nn.Linear(256, 128)
self.fc21 = nn.Linear(128, 2)
self.fc22 = nn.Linear(128, 2)
self.fc3 = nn.Linear(2, 128)
self.fc4 = nn.Linear(128, 256)
self.fc5 = nn.Linear(256, 512)
self.gelu = GELU()
def encode(self, x):
h1 = self.gelu(self.fc0(x))
h2 = self.gelu(self.fc1(h1))
return self.fc21(h2), self.fc22(h2)
def reparametrize(self, mu, logvar):
std = logvar.mul(0.5).exp_()
if torch.cuda.is_available():
eps = torch.cuda.FloatTensor(std.size()).normal_()
else:
eps = torch.FloatTensor(std.size()).normal_()
eps = Variable(eps)
return eps.mul(std).add_(mu)
def decode(self, z):
h3 = self.gelu(self.fc3(z))
h4 = self.gelu(self.fc4(h3))
return torch.sigmoid(self.fc5(h4))
def forward(self, x):
mu, logvar = self.encode(x)
z = self.reparametrize(mu, logvar)
return self.decode(z), mu, logvar
model = VAE()
if torch.cuda.is_available(): model.cuda()
optimizer = optim.Adam(model.parameters(), lr=1e-2)
reconstruction_function = nn.MSELoss(size_average=False)
def loss_function(recon_x, x, mu, logvar):
BCE = reconstruction_function(recon_x, x)
KLD_element = mu.pow(2).add_(logvar.exp()).mul_(-1).add_(1).add_(logvar)
return BCE + torch.sum(KLD_element).mul_(-0.5)
for epoch in range(500):
model.train()
train_loss = 0
for batch_idx, data in enumerate(train_loader):
if torch.cuda.is_available():
data = data[0].cuda()
optimizer.zero_grad()
recon_batch, mu, logvar = model(data)
loss = loss_function(recon_batch, data, mu, logvar)
loss.backward()
train_loss += loss.item()
optimizer.step()
print('====> Epoch: {} Average loss: {:.4f}'.format(
epoch, train_loss / len(train_loader.dataset)))
mus = model(X_test.cuda())[1]
feat = mus.cpu().detach().numpy()
plt.figure(figsize=(24, 12))
mus = np.concatenate([feat, aud.reshape(-1, 1)], axis=1)
pd.DataFrame(mus, columns=['x', 'y', 'c']).plot.scatter(
x='x', y='c', c='c', colormap='seismic', s=0.1, ylim=(-0.02, 0.02))
plt.savefig(root+'graphs/vae_aud')
plt.close()
plt.figure(figsize=(24, 12))
mus = np.concatenate([feat, nzd.reshape(-1, 1)], axis=1)
pd.DataFrame(mus, columns=['x', 'y', 'c']).plot.scatter(
x='x', y='c', c='c', colormap='seismic', s=0.1, ylim=(-0.02, 0.02))
plt.savefig(root+'graphs/vae_nzd')
plt.close()
================================================
FILE: dataset/X_test.pkl
================================================
[File too large to display: 16.4 MB]
================================================
FILE: example_cpu.py
================================================
# https://github.com/kayuksel/forex-rl-challenge
root = '/home/kamer/Videos/fx/'
from past.builtins import execfile
execfile('shared.py')
# Constants defining the neural network and multiprocessing
No_Channels = len(assets)
No_Features = 512 + No_Channels
# No. processes, reduce this if it doesnt fit to your GPU!!!
No_Proccess = 1
epochs = 50
# Transaction cost that is utilized for commission expenses
cost = 0.0004
# Function for calculating risk-measures and plotting results
STEP_SIZE = 0.01
# Go through assigned batches for each process to calculate
# the reward that occurs from agent's portfolio decisions
def calculate_reward(model, loader, index, risk = True, policy = None, plot = False):
epoch_weights = []
last_action = torch.ones(No_Channels).to('cpu')
last_action /= float(No_Channels)
total_reward, pos_reward = 0.0, 0.0
pos_count = 0
for i, (features, rewards) in enumerate(loader):
features = features.view(-1).to('cpu')
rewards = rewards.float().to('cpu')
# Feed the last action back to the model as an input
state = torch.cat([features, last_action]).double()
# Get a new action from the model given current state
action = model.forward(state, policy) if torch.is_tensor(policy) else model.forward(state)
# Tanh activation is utilized for long/short portfolio
weights = torch.tanh(action[:-1])
# Up to 2x leverage is allowed for each action (position)
certain = 0.5 + torch.sigmoid(action[-1]) / 2.0
# Absolute portfolio value should sum to one x leverage
weights = weights / (weights.abs().sum() * certain)
# Calculate the transaction cost due to portfolio change
reward = (weights.double() - last_action.double()).abs().sum() * cost
# Calculate portfolio return relative to the market itself
reward -= (weights.double() * rewards.double()).sum() #- rewards.abs().mean()
# Calculate portfolio return relative to the last weights
reward += (last_action.double() * rewards.double()).sum()
# Save the current action to employ it for the next step
last_action = weights.float()
# Save the action history to measure and plot afterwards
epoch_weights.append(weights.detach().cpu().numpy())
if len(epoch_weights) == 1: continue
# Future-work: risk-sensitive rl using exponential utility
total_reward = total_reward + (reward.abs() if risk else reward)
if reward > 0:
pos_reward = pos_reward + reward
pos_count += 1
total_reward = total_reward / len(loader)
pos_reward = pos_reward / pos_count
return total_reward, pos_reward, epoch_weights
#worker for multithreading
def worker(q_policies, q_results, calc_reward_args):
while True:
data = q_policies.get()
if data is None:
#print('Worker Exiting')
q_policies.task_done()
break
policy_d = data[0]
delta_for_policy = data[1]
#print('Worker recieved work - Starting Calculation')
total_reward, pos_reward, _ = calculate_reward(calc_reward_args[0],calc_reward_args[1],
calc_reward_args[2], calc_reward_args[3],
policy_d)
train_reward = total_reward / pos_reward if calc_reward_args[3] else total_reward
q_results.put([train_reward.item(), delta_for_policy, data[2]])
q_policies.task_done()
#biggest changes to the train algo
def train(model, index, risk = True):
# Calculate the average reward for the batches of this process
n_process = 24
step_size = STEP_SIZE
num_best_deltas = n_process // 2
deltas = model.genDeltas()
policy = model.weights
if (len(deltas) < num_best_deltas): raise ValueError('Number of usable deltas less than total amount of deltas')
q_Deltas_Policies = JoinableQueue()
q_Rewards = Queue(maxsize=len(deltas)*2)
procs = []
#rollout policies with noise and calculate reward
for i in range(n_process):
calculate_reward_args = [model, train_loader, index, risk]
p = Process(target = worker, args=(q_Deltas_Policies, q_Rewards, calculate_reward_args,))
p.start()
procs.append(p)
for idx, delta in enumerate(deltas):
polPlus = policy + delta
polMinus = policy - delta
q_Deltas_Policies.put([polPlus,idx, 0])
q_Deltas_Policies.put([polMinus,idx, 1])
for i in range(n_process): q_Deltas_Policies.put(None)
q_Deltas_Policies.join()
r = []
for d in range(len(deltas)*2):
[rew, d, sign] = q_Rewards.get()
r.append([rew,d, sign])
deltaDF = pd.DataFrame(columns=['Rewards', 'Deltas', 'PlusMinus'])
for i in range(len(r)): deltaDF.loc[i] = [r[i][x] for x in range(3)]
rewardStd = deltaDF['Rewards'].std()
groupedDeltas = deltaDF.groupby('Deltas', as_index=True).sum()
delta_reward_policy = torch.zeros_like(policy)
#calculate the policy update step
for idx, row in deltaDF.groupby('Deltas').sum().iterrows():
if idx > num_best_deltas: break
plus = deltaDF.loc[(deltaDF['Deltas'] == idx) & (deltaDF['PlusMinus'] == 0), 'Rewards'].values
minus = deltaDF.loc[(deltaDF['Deltas'] == idx) & (deltaDF['PlusMinus'] == 1), 'Rewards'].values
delta_reward_policy += (plus[0]-minus[0]) * deltas[int(idx)]
#apply the update step to the policy
return policy + (step_size / (num_best_deltas*rewardStd)) * delta_reward_policy
class ARSModel():
def __init__(self, numFeatures, NumChannels, preload):
self.feats = numFeatures
self.chnls = NumChannels
self.weights = torch.zeros(self.feats, self.chnls+1).to('cpu')
self.weights[:,:] = torch.Tensor(preload.T)[:,:self.chnls+1]
self.numDeltas = 36
self.std = 0.5
self.noiseStd = torch.tensor(self.std, dtype = torch.float64).to('cpu')
def genDeltas(self):
deltas = torch.randn(self.numDeltas, self.feats, self.chnls+1).to('cpu')
return deltas * self.noiseStd.float()
def forward(self, state, policy = None):
if torch.is_tensor(policy): return torch.matmul(state.double(), policy.double())
return torch.matmul(state.double(), self.weights.double())
best_reward = 1.0
if __name__ == '__main__':
with open(root+'models/weights.pkl', 'rb') as f: weights = cPickle.load(f)
model = ARSModel(No_Features, No_Channels, weights)
#_, _, epoch_weights = calculate_reward(model, test_loader, No_Proccess+1, risk=True)
#plot_function(epoch_weights)
for epoch in range(epochs):
start_time = time.time()
n_policy = train(model, 0)
total_reward, pos_reward, epoch_weights = calculate_reward(
model, test_loader, No_Proccess+1, risk=True, policy=n_policy)
test_reward = pos_reward / total_reward
model.weights = n_policy
end_time = time.time()
threadtime = end_time-start_time
ttime = threadtime * (epochs-1-epoch)
h = int(ttime/(60*60))
m = int((ttime - h*60*60)/60)
s = int((ttime-h*60*60-m*60))
print('Epoch {} took {} seconds. Time remaining {}h:{}m:{}s'.format(epoch+1, int(threadtime), h,m,s))
STEP_SIZE *= 0.995
print('Step size decreased to {}'.format(STEP_SIZE))
if test_reward > best_reward: continue
best_reward = test_reward
weight_names = root+'models/policy_weights'
np.save(weight_names, n_policy.numpy())
plot_function(epoch_weights)
================================================
FILE: example_gpu.py
================================================
# https://github.com/kayuksel/forex-rl-challenge
root = '/home/kamer/Videos/fx/'
from past.builtins import execfile
execfile('shared.py')
# Constants defining the neural network and multiprocessing
No_Features = 512
No_Channels = len(assets)
# No. processes, reduce this if it doesnt fit to your GPU!!!
No_Proccess = 8
epochs = 100
# Transaction cost that is utilized for commission expenses
cost = 0.0004
# Go through assigned batches for each process to calculate
# the reward that occurs from agent's portfolio decisions
'''
Action is a vector which consists of the distribution of portolio weights to the assets,
and the leverage up to 2x. Since that we have a long-short portfolio, the absolute sum of
the portfolio weights should sum to one (If it was a long-only portfolio, it would have
only positive values which would sum to one). That's why we first give the model output
to the tanh and then normalize the resulting vector with the sum of its absolute values.
'''
def calculate_reward(model, loader, index, risk = 1.0, skip = None):
epoch_weights = []
last_action = torch.ones(No_Channels).cuda()
last_action /= float(No_Channels)
total_reward, pos_reward = 0.0, 0.0
pos_count = 0
for i, (features, rewards) in enumerate(loader):
if skip is not None and skip[i]: continue
features = features.view(-1).cuda(non_blocking=True)
rewards = rewards.float().cuda(non_blocking=True)
# Feed the last action back to the model as an input
state = torch.cat([features, last_action])
# Get a new action from the model given current state
action = model(state)
# Tanh activation is utilized for long/short portfolio
weights = torch.tanh(action[:-1])
# Up to 2x leverage is allowed for each action (position)
certain = 0.5 + torch.sigmoid(action[-1]) / 2.0
# Absolute portfolio value should sum to one x leverage
weights = weights / (weights.abs().sum() * certain)
# Calculate the transaction cost due to portfolio change
reward = (weights - last_action).abs().sum() * cost
# Calculate portfolio return relative to the market itself
reward -= (weights * rewards).sum() #- rewards.abs().mean()
# Calculate portfolio return relative to the last weights
reward += (last_action * rewards).sum()
# Save the current action to employ it for the next step
last_action = weights
# Save the action history to measure and plot afterwards
epoch_weights.append(weights.detach().cpu().numpy())
# Do not sum the reward concluded from the first action
if len(epoch_weights) == 1: continue
# Future-work: risk-sensitive rl using exponential utility
total_reward = total_reward + (reward.abs() if risk else reward)
if reward > 0:
pos_reward = pos_reward + reward**risk
pos_count += 1
torch.cuda.empty_cache()
# Calculate the average reward for the non-skipped batches
skipped = 0 if skip is None else sum(skip)
total_reward = total_reward / (len(loader) - skipped)
pos_reward = pos_reward.pow(1/risk) / pos_count
if skip is None: plot_function(epoch_weights)
return total_reward, pos_reward
'''
Reward at each time step, is the sum of element-wise multiplication of
portfolio weights and asset returns (minus the transaction cost, which
is the absolute sum of portfolio weight differences times the commission.
'''
def train(model, optimizer, index, risk = 1.0):
# Risk-sensitivity disabled if the risk factor is less than 1.0
if risk < 1.0: risk = 0.0
# Mark the batches that are going to be skipped in this process
skip = [(i // (len(train_loader)//No_Proccess)) != index for i in range(len(train_loader))]
# Calculate the average reward for the batches of this process
total_reward, pos_reward = calculate_reward(model, train_loader, index, risk, skip)
train_reward = pos_reward / total_reward if risk else total_reward
#print('train %f' % -train_reward.item())
# Perform an optimizer on the shared model with calculated loss
optimizer.zero_grad()
train_reward.backward()
#nn.utils.clip_grad_norm_(model.parameters(), max_grad)
optimizer.step()
torch.cuda.empty_cache()
best_reward = 1.0
if __name__ == '__main__':
# A simple linear layer is employed as an example model for you
model = nn.Linear(No_Features + No_Channels, No_Channels+1, bias = False).cuda().share_memory()
# Define the optimizer that will be utilized by all processes
optimizer = AdamW(params = model.parameters(), lr = 1e-3,
eps = 5e-3, weight_decay = 1e-5, hypergrad = 1e-3, partial = 2/3)
for epoch in range(epochs):
model.train(True)
# For each epoch start all of the processes to update model
processes = []
for i in range(No_Proccess):
p = mp.Process(target=train, args=(model, optimizer, i))
p.start()
processes.append(p)
for p in processes: p.join()
# After all of processes are done, evaluate model on test set
model.eval()
total_reward, pos_reward = calculate_reward(model, test_loader, No_Proccess+1)
test_reward = pos_reward / total_reward
if test_reward > best_reward: continue
best_reward = test_reward
lin_weights = model.weight.data.detach().cpu().numpy()
with open(root+'models/weights.pkl', 'wb') as f:
cPickle.dump(lin_weights, f)
# Save best model for participating into the competition
torch.save(model.state_dict(), root+'models/model.pt')
================================================
FILE: outputs/hourly_returns.csv
================================================
,0
2018-01-01 00:00:00,0.0005848508245244429
2018-01-01 01:00:00,0.0008345336553621889
2018-01-01 02:00:00,0.0003168668620411769
2018-01-01 03:00:00,0.0006887981387708208
2018-01-01 04:00:00,-0.00032800158441577984
2018-01-01 05:00:00,-6.011208648340979e-05
2018-01-01 06:00:00,0.0011996161842481707
2018-01-01 07:00:00,9.771310802528167e-06
2018-01-01 08:00:00,-0.002141640087746956
2018-01-01 09:00:00,-0.0005694571441095854
2018-01-01 10:00:00,0.000773868218498969
2018-01-01 11:00:00,0.00018812463113405465
2018-01-01 12:00:00,-0.0015363637783181862
2018-01-01 13:00:00,-0.00014346775092724076
2018-01-01 14:00:00,-1.999480971726432e-05
2018-01-01 15:00:00,0.006916722855114094
2018-01-01 16:00:00,-0.00017112384068189597
2018-01-01 17:00:00,-0.020209269977461634
2018-01-01 18:00:00,-0.00035886019037311236
2018-01-01 19:00:00,0.0005774263004542251
2018-01-01 20:00:00,0.0002386492415614042
2018-01-01 21:00:00,0.0005099226414704221
2018-01-01 22:00:00,0.000304159635176723
2018-01-01 23:00:00,0.00018964077328295775
2018-01-02 00:00:00,-0.0004973458593868827
2018-01-02 01:00:00,-0.0003785284802612665
2018-01-02 02:00:00,0.00011839810640939463
2018-01-02 03:00:00,-0.00010627281627927597
2018-01-02 04:00:00,-8.841262628857022e-05
2018-01-02 05:00:00,-0.0003865639291866826
2018-01-02 06:00:00,0.00036203312568974767
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gitextract_50dtzs9b/ ├── .gitignore ├── LICENSE ├── README.md ├── adamw.py ├── autoencoder.py ├── dataset/ │ ├── X_test.pkl │ ├── y_test.pkl │ └── y_train.pkl ├── example_cpu.py ├── example_gpu.py ├── models/ │ ├── model.pt │ ├── policy_weights.npy │ └── weights.pkl ├── outputs/ │ ├── hourly_returns.csv │ └── portfolio_weights.csv ├── requirements.txt └── shared.py
SYMBOL INDEX (22 symbols across 5 files)
FILE: adamw.py
class AdamW (line 5) | class AdamW(Optimizer):
method __init__ (line 6) | def __init__(self, params, lr=1e-4, betas=(0.9, 0.999), eps=1e-5, weig...
method step (line 13) | def step(self, closure=None):
FILE: autoencoder.py
class GELU (line 34) | class GELU(nn.Module):
method forward (line 35) | def forward(self, x):
class VAE (line 38) | class VAE(nn.Module):
method __init__ (line 39) | def __init__(self):
method encode (line 49) | def encode(self, x):
method reparametrize (line 53) | def reparametrize(self, mu, logvar):
method decode (line 61) | def decode(self, z):
method forward (line 65) | def forward(self, x):
function loss_function (line 74) | def loss_function(recon_x, x, mu, logvar):
FILE: example_cpu.py
function calculate_reward (line 20) | def calculate_reward(model, loader, index, risk = True, policy = None, p...
function worker (line 61) | def worker(q_policies, q_results, calc_reward_args):
function train (line 80) | def train(model, index, risk = True):
class ARSModel (line 130) | class ARSModel():
method __init__ (line 131) | def __init__(self, numFeatures, NumChannels, preload):
method genDeltas (line 140) | def genDeltas(self):
method forward (line 144) | def forward(self, state, policy = None):
FILE: example_gpu.py
function calculate_reward (line 24) | def calculate_reward(model, loader, index, risk = 1.0, skip = None):
function train (line 75) | def train(model, optimizer, index, risk = 1.0):
FILE: shared.py
function plot_function (line 59) | def plot_function(epoch_weights):
Condensed preview — 17 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (1,100K chars).
[
{
"path": ".gitignore",
"chars": 20,
"preview": "dataset/X_train.pkl\n"
},
{
"path": "LICENSE",
"chars": 35149,
"preview": " GNU GENERAL PUBLIC LICENSE\n Version 3, 29 June 2007\n\n Copyright (C) 2007 Free "
},
{
"path": "README.md",
"chars": 3031,
"preview": "# FX Reinforcement Learning Playground\n\nThis repository contains an open challenge for a Portfolio Balancing AI in Forex"
},
{
"path": "adamw.py",
"chars": 2703,
"preview": "import math\nimport torch\nfrom torch.optim.optimizer import Optimizer\n\nclass AdamW(Optimizer):\n def __init__(self, par"
},
{
"path": "autoencoder.py",
"chars": 3844,
"preview": "import matplotlib\nmatplotlib.use('Agg')\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport to"
},
{
"path": "example_cpu.py",
"chars": 7669,
"preview": "# https://github.com/kayuksel/forex-rl-challenge\nroot = '/home/kamer/Videos/fx/'\nfrom past.builtins import execfile\nexec"
},
{
"path": "example_gpu.py",
"chars": 5677,
"preview": "# https://github.com/kayuksel/forex-rl-challenge\nroot = '/home/kamer/Videos/fx/'\nfrom past.builtins import execfile\nexec"
},
{
"path": "outputs/hourly_returns.csv",
"chars": 357477,
"preview": ",0\n2018-01-01 00:00:00,0.0005848508245244429\n2018-01-01 01:00:00,0.0008345336553621889\n2018-01-01 02:00:00,0.00031686686"
},
{
"path": "outputs/portfolio_weights.csv",
"chars": 661481,
"preview": ",AUDUSD,NZDUSD,USDUSD\n2018-01-01 00:00:00,0.018217800593405116,-0.7775373557783031,0.5013353184208913\n2018-01-01 01:00:0"
},
{
"path": "requirements.txt",
"chars": 138,
"preview": "pandas==0.23.4\ntorch==1.0.0\nempyrical==0.5.0\ntorchvision==0.2.1\nseaborn==0.9.0\nfuture==0.17.1\nmatplotlib==3.0.2\ntqdm==4."
},
{
"path": "shared.py",
"chars": 4446,
"preview": "import matplotlib\nmatplotlib.use('Agg')\nimport numpy as np\nimport pandas as pd\nimport empyrical as emp\nimport matplotlib"
}
]
// ... and 6 more files (download for full content)
About this extraction
This page contains the full source code of the kayuksel/forex-rl-challenge GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 17 files (17.4 MB), approximately 541.6k tokens, and a symbol index with 22 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.