Hi, I am having problem Strategy trades illiquid instruments at latest date in log, while I have multiplied weight with liquid. I checked log only latest date has this error. Please help me. My stock universe is top 7 magnificent. @support @Vyacheslav_B
My alpha id is #18379797
@support Hello. My strategy has the id #16934018 and was submitted in early May, but pnl OS has not been updated yet. Please check this issue. Thank you.
@support Thank you very much. I would like to ask the following: Currently I want to research crypto and futures strategies, but currently the contest is about stocks, so I would like to ask how I can submit crypto and futures strategies to be able to see the OS PnL of these strategies? Thank you.
Hi, I noticed that the time to enter Quantiacs was very long (from entering the platform to successfully logging in took about a few minutes, then accessing the notebook repo, accessing the init and strategy files also took a few more minutes). Hopefully Quantiacs can speed up access to files on the platform as well as when logging in.
Thank you very much.
@vyacheslav_b Thank you for your response
Here is the code I used from your example. I added some other features (eg: trend = qnta.roc(qnta.lwma(data.sel(field='close'), 40), 1),...) and noticed that after passing ml_backtest, every indexes are all nan. Pnl is a straight line. I have tried changing many other features but the result is still the same, all indicators are nan
import xarray as xr
import qnt.data as qndata
import qnt.backtester as qnbt
import qnt.ta as qnta
import qnt.stats as qns
import qnt.graph as qngraph
import qnt.output as qnout
import numpy as np
import pandas as pd
import torch
from torch import nn, optim
import random
asset_name_all = ['NAS:AAPL', 'NAS:GOOGL']
lookback_period = 155
train_period = 100
class LSTM(nn.Module):
"""
Class to define our LSTM network.
"""
def __init__(self, input_dim=3, hidden_layers=64):
super(LSTM, self).__init__()
self.hidden_layers = hidden_layers
self.lstm1 = nn.LSTMCell(input_dim, self.hidden_layers)
self.lstm2 = nn.LSTMCell(self.hidden_layers, self.hidden_layers)
self.linear = nn.Linear(self.hidden_layers, 1)
def forward(self, y):
outputs = []
n_samples = y.size(0)
h_t = torch.zeros(n_samples, self.hidden_layers, dtype=torch.float32)
c_t = torch.zeros(n_samples, self.hidden_layers, dtype=torch.float32)
h_t2 = torch.zeros(n_samples, self.hidden_layers, dtype=torch.float32)
c_t2 = torch.zeros(n_samples, self.hidden_layers, dtype=torch.float32)
for time_step in range(y.size(1)):
x_t = y[:, time_step, :] # Ensure x_t is [batch, input_dim]
h_t, c_t = self.lstm1(x_t, (h_t, c_t))
h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2))
output = self.linear(h_t2)
outputs.append(output.unsqueeze(1))
outputs = torch.cat(outputs, dim=1).squeeze(-1)
return outputs
def get_model():
def set_seed(seed_value=42):
"""Set seed for reproducibility."""
random.seed(seed_value)
np.random.seed(seed_value)
torch.manual_seed(seed_value)
torch.cuda.manual_seed(seed_value)
torch.cuda.manual_seed_all(seed_value) # if you are using multi-GPU.
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
set_seed(42)
model = LSTM(input_dim=3)
return model
def get_features(data):
close_price = data.sel(field="close").ffill('time').bfill('time').fillna(1)
open_price = data.sel(field="open").ffill('time').bfill('time').fillna(1)
high_price = data.sel(field="high").ffill('time').bfill('time').fillna(1)
log_close = np.log(close_price)
log_open = np.log(open_price)
trend = qnta.roc(qnta.lwma(close_price ), 40), 1)
features = xr.concat([log_close, log_open, high_price, trend], "feature")
return features
def get_target_classes(data):
price_current = data.sel(field='open')
price_future = qnta.shift(price_current, -1)
class_positive = 1 # prices goes up
class_negative = 0 # price goes down
target_price_up = xr.where(price_future > price_current, class_positive, class_negative)
return target_price_up
def load_data(period):
return qndata.stocks.load_ndx_data(tail=period, assets=asset_name_all)
def train_model(data):
features_all = get_features(data)
target_all = get_target_classes(data)
models = dict()
for asset_name in asset_name_all:
model = get_model()
target_cur = target_all.sel(asset=asset_name).dropna('time', 'any')
features_cur = features_all.sel(asset=asset_name).dropna('time', 'any')
target_for_learn_df, feature_for_learn_df = xr.align(target_cur, features_cur, join='inner')
criterion = nn.MSELoss()
optimiser = optim.LBFGS(model.parameters(), lr=0.08)
epochs = 1
for i in range(epochs):
def closure():
optimiser.zero_grad()
feature_data = feature_for_learn_df.transpose('time', 'feature').values
in_ = torch.tensor(feature_data, dtype=torch.float32).unsqueeze(0)
out = model(in_)
target = torch.zeros(1, len(target_for_learn_df.values))
target[0, :] = torch.tensor(np.array(target_for_learn_df.values))
loss = criterion(out, target)
loss.backward()
return loss
optimiser.step(closure)
models[asset_name] = model
return models
def predict(models, data, state):
last_time = data.time.values[-1]
data_last = data.sel(time=slice(last_time, None))
weights = xr.zeros_like(data_last.sel(field='close'))
for asset_name in asset_name_all:
features_all = get_features(data_last)
features_cur = features_all.sel(asset=asset_name).dropna('time', 'any')
if len(features_cur.time) < 1:
continue
feature_data = features_cur.transpose('time', 'feature').values
in_ = torch.tensor(feature_data, dtype=torch.float32).unsqueeze(0)
out = models[asset_name](in_)
prediction = out.detach()[0]
weights.loc[dict(asset=asset_name, time=features_cur.time.values)] = prediction
weights = weights * data_last.sel(field="is_liquid")
# state may be null, so define a default value
if state is None:
default = xr.zeros_like(data_last.sel(field='close')).isel(time=-1)
state = {
"previus_weights": default,
}
previus_weights = state['previus_weights']
# align the arrays to prevent problems in case the asset list changes
previus_weights, weights = xr.align(previus_weights, weights, join='right')
weights_avg = (previus_weights + weights) / 2
next_state = {
"previus_weights": weights_avg.isel(time=-1),
}
# print(last_time)
# print("previus_weights")
# print(previus_weights)
# print(weights)
# print("weights_avg")
# print(weights_avg.isel(time=-1))
return weights_avg, next_state
weights = qnbt.backtest_ml(
load_data=load_data,
train=train_model,
predict=predict,
train_period=train_period,
retrain_interval=360,
retrain_interval_after_submit=1,
predict_each_day=True,
competition_type='stocks_nasdaq100',
lookback_period=lookback_period,
start_date='2006-01-01',
build_plots=True
)
@vyacheslav_b Hello, I was trying the code you gave and realized that using state for train ml_backtest only works when the get feature function is a feature like ohlc or log of ohlc (open, high, low, close).
I added some other features (eg: trend = qnta.roc(qnta.lwma(data.sel(field='close'), 40), 1),...) and noticed that after passing ml_backtest, every The indexes are all nan. Looking forward to your help. Thank you.
Hi,
I know that there is currently a stock contest going on, but if I want to develop a crypto or futures strategy and then submit it so I can test OS performance, is that allowed?
Thank you.
@support @Vyacheslav_B
Hi Quantiacs,
I notice that Q21 is about to run out of submission time (May 1), but now I realize that the time to submit a strategy is very long (takes 3 - 5 days). Hopefully Quantiacs can provide a quick submission strategy solution (I used single backtest, running precheck is also very fast), or it would be really good if Q21 could extend the submission time by a few weeks.
Besides, I would also like to ask if ML and DL algorithms switch from ml_backtest to single backtest, will they overfit when using all the data for training? In ml_backtest, it is allowed to divide train_period and test_period, so how can we divide like that in single backtest? We look forward to your help providing a single backtest code example for ML DL strategy. Thank you. Besides, we hope that Quantiacs will provide single backtest for ML DL to help limit the forward looking situation.
Thanks a lot.
@vyacheslav_b I also encountered this situation. Can you give more specific instructions on how the precheck file works? Thank you
Hi,
I got an error after running load data on the local environment
Please help me fix it.
Thank you @support
@vyacheslav_b Thank you so much. I would like to ask more, every time I train ml_backtest for LSTM, I get a different sharpe result (sometimes the sharpness is 1.2, 1.1, 0.9,...) Why is there the following sharpness inconsistency? Every time you train like that? We wish to be answered. Thank you.
@illustrious-felice I would like to ask, how can I remove the correlation check each time I run ml_backtest? Thank you so much. Checking correlation is time consuming and I want to eliminate it
@illustrious-felice If I leave the list of stocks as these stocks, the backtest will run normally and I will get the results
However, if I change to other codes, such as TSLA, FB, META, it will error
I could only find about 6 or 7 stock codes that didn't have errors
@vyacheslav_b Thank you so much. I have one more question. When I add stock (NAS:TSLA, NAS:FB,...), I get the following error:
It seems that these tickers are missing data. I tried other codes and found some that could be added like ADBE, GOOGL. I would like to ask why this situation occurs and is there any way to fix it? Thank you very much.
@vyacheslav_b Hopefully you can give me a code example of LSTM when using more than 1 feature to train in the get_feaures() function and using more than 3 assets (stock codes). Thank you. Currently I am getting an error when changing the number of features and assets. Thank you so much.
I hope you can show me an example of changing ml_backtest to single backtest in example neural networks too.
@vyacheslav_b Thank you. Please help me with the issue RuntimeError: expand(torch.DoubleTensor{[694, 6]}, size=[694]): the number of sizes provided (1) must be greater or equal to the number of dimensions in the tensor (2).
I have tried adding the following feature:
However I got an error
Please help me fix the error. I really need to fix this bug so I can train more than 1 feature. Thank you very much.