def quantstats_analysis(env, df, rl_name, start_index, end_index):
qs.extend_pandas()
net_worth = pd.Series(env.history['total_profit'],
index=df.index[start_index + 1:end_index])
returns = net_worth.pct_change().iloc[1:]
qs.reports.full(returns)
qs.reports.html(returns,
output=f'./data/result_analysis/{rl_name}_quantstats.html')
def extend_pandas():
"""Enhances pandas objects."""
import quantstats as qs
from pandas.core.base import PandasObject as _po
qs.extend_pandas()
_po.gmean = stats.gmean
def stock_report(symbol):
import quantstats as qs
# extend pandas functionality with metrics, etc.
qs.extend_pandas()
# fetch the daily returns for a stock
stock = qs.utils.download_returns(symbol)
#qs.core.plot_returns_bars(stock, "SPY")
qs.reports.html(stock, "SPY", output="report.html")
import quantstats as qs
# extend pandas functionality with metrics, etc.
qs.extend_pandas()
# fetch the daily returns for a stock
stock = qs.utils.download_returns('FB')
stock.to_csv("FB.csv")
print(stock)
qs.reports.full(stock)
# show sharpe ratio
#qs.stats.sharpe(stock)
# or using extend_pandas() :)
#stock.sharpe(
