| Overall Statistics |
|
Total Trades 0 Average Win 0% Average Loss 0% Compounding Annual Return 0% Drawdown 0% Expectancy 0 Net Profit 0% Sharpe Ratio 0 Probabilistic Sharpe Ratio 0% Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0 Annual Variance 0 Information Ratio -1.517 Tracking Error 0.142 Treynor Ratio 0 Total Fees $0.00 Estimated Strategy Capacity $0 |
class EmptyAlgoToShareNotebooks(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2020,12,1) # Set Start Date
self.SetCash(1) # Set Strategy Cash
def OnData(self, data):
passimport matplotlib.dates as mdates
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns
def plot_df(df, color='blue', size=(16, 7), legend='Close Price', y_label='Price in USD', title=None, kind='line'):
plt.style.use('dark_background')
plt.rcParams["figure.figsize"] = size
ax = df.plot(kind=kind)
plt.title(title)
plt.ylabel(y_label)
x = 0.1
y = 0.05
plt.text(x, y, 'www.ostirion.net', fontsize=15, transform=ax.transAxes)
plt.legend(ncol=int(len(df.columns) / 2))
date_form = mdates.DateFormatter("%m-%Y")
plt.xticks(rotation=45);
plt.show()
def plot_corr_hm(df, title='Title', size=(16, 7), annot = True):
corr = df.corr()
plt.style.use('dark_background')
plt.rcParams["figure.figsize"] = size
mask = np.triu(np.ones_like(corr, dtype=bool))
cmap = sns.color_palette("RdBu")
ax = sns.heatmap(corr, mask=mask, vmax=.3, center=0, cmap=cmap, annot=annot,
square=True, linewidths=0, cbar_kws={"shrink": .5}, fmt='g')
ax.set_title(title)
plt.setp(ax.get_yticklabels(), rotation=0);
plt.setp(ax.get_xticklabels(), rotation=90);
plt.show()
def plot_cm(df, title='Title', size=(16,7)):
plt.style.use('dark_background')
plt.rcParams["figure.figsize"] = size
cmap = sns.color_palette("Blues")
ax = sns.heatmap(df, cmap=cmap, annot=True, linewidths=0, cbar_kws={"shrink": .5}, fmt='g')
ax.set_title(title)
plt.xlabel('Predicted')
plt.ylabel('True')
plt.setp(ax.get_xticklabels(), rotation=0);
def plot_hm(df, title='Title', size=(16, 7), annot = True, x_rot=90):
plt.style.use('dark_background')
plt.rcParams["figure.figsize"] = size
cmap = sns.color_palette("RdBu")
ax = sns.heatmap(df, vmax=.3, center=0, cmap=cmap, annot=annot,
square=True, linewidths=0, cbar_kws={"shrink": .5}, fmt='g')
ax.set_title(title)
plt.setp(ax.get_yticklabels(), rotation=0);
plt.setp(ax.get_xticklabels(), rotation=x_rot);
plt.show()