ysdede

import json
import signal
import sys
from subprocess import PIPE, Popen, call

import jessetk.utils as utils
import optuna

# Multi-objective NSGAII hyperparameters optimization with Optuna
# Wraps Jesse ai's backtest function as an objective function

ysdede

import optuna
import statistics
import json

# This code snippet filters Optuna trials based on performance metrics and standard deviation
# and generates a results.csv file containing all results
# and a SEQ.py file containing all hyperparameters

study = optuna.create_study(study_name="Band5min-LongOnly", directions=["maximize", "maximize"],
                                storage="postgresql://optuna_user:optuna_password@localhost/optuna_db_3", load_if_exists=True)

rnikitin

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    *margin-right: .3em;
    line- vertical-align: text-top;
    background-image: url("images/glyphicons-2.png");
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aheckmann

var mongoose = require('mongoose');
mongoose.connect('localhost', 'testing_multiTenant');

/**
 * User schema.
 */

var UserSchema = new mongoose.Schema({
    name: String
  , prefix: { type: String, required: true }