/
plot.py
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plot.py
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from copy import deepcopy
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pyfolio
from finrl.config import TRAIN_START_DATE
from finrl.meta.preprocessor.yahoodownloader import YahooDownloader
from pyfolio import timeseries
from meta import config
def get_daily_return(df, value_col_name="account_value"):
df = deepcopy(df)
df["daily_return"] = df[value_col_name].pct_change(1)
df["date"] = pd.to_datetime(df["date"])
df.set_index("date", inplace=True, drop=True)
df.index = df.index.tz_localize("UTC")
return pd.Series(df["daily_return"], index=df.index)
def convert_daily_return_to_pyfolio_ts(df):
strategy_ret = df.copy()
strategy_ret["date"] = pd.to_datetime(strategy_ret["date"])
strategy_ret.set_index("date", drop=False, inplace=True)
strategy_ret.index = strategy_ret.index.tz_localize("UTC")
del strategy_ret["date"]
return pd.Series(strategy_ret["daily_return"].values, index=strategy_ret.index)
def backtest_stats(account_value, value_col_name="account_value"):
dr_test = get_daily_return(account_value, value_col_name=value_col_name)
perf_stats_all = timeseries.perf_stats(
returns=dr_test,
positions=None,
transactions=None,
turnover_denom="AGB",
)
print(perf_stats_all)
return perf_stats_all
def backtest_plot(
account_value,
baseline_start=config.TRADE_START_DATE,
baseline_end=config.TRADE_END_DATE,
baseline_ticker="^DJI",
value_col_name="account_value",
):
df = deepcopy(account_value)
df["date"] = pd.to_datetime(df["date"])
test_returns = get_daily_return(df, value_col_name=value_col_name)
baseline_df = get_baseline(
ticker=baseline_ticker, start=baseline_start, end=baseline_end
)
baseline_df["date"] = pd.to_datetime(baseline_df["date"], format="%Y-%m-%d")
baseline_df = pd.merge(df[["date"]], baseline_df, how="left", on="date")
baseline_df = baseline_df.fillna(method="ffill").fillna(method="bfill")
baseline_returns = get_daily_return(baseline_df, value_col_name="close")
with pyfolio.plotting.plotting_context(font_scale=1.1):
pyfolio.create_full_tear_sheet(
returns=test_returns,
benchmark_rets=baseline_returns,
set_context=False,
)
def get_baseline(ticker, start, end):
return YahooDownloader(
start_date=start, end_date=end, ticker_list=[ticker]
).fetch_data()
def trx_plot(df_trade, df_actions, ticker_list):
df_trx = pd.DataFrame(np.array(df_actions["transactions"].to_list()))
df_trx.columns = ticker_list
df_trx.index = df_actions["date"]
df_trx.index.name = ""
for i in range(df_trx.shape[1]):
df_trx_temp = df_trx.iloc[:, i]
df_trx_temp_sign = np.sign(df_trx_temp)
buying_signal = df_trx_temp_sign.apply(lambda x: x > 0)
selling_signal = df_trx_temp_sign.apply(lambda x: x < 0)
tic_plot = df_trade[
(df_trade["tic"] == df_trx_temp.name)
& (df_trade["date"].isin(df_trx.index))
]["close"]
tic_plot.index = df_trx_temp.index
plt.figure(figsize=(10, 8))
plt.plot(tic_plot, color="g", lw=2.0)
plt.plot(
tic_plot,
"^",
markersize=10,
color="m",
label="buying signal",
markevery=buying_signal,
)
plt.plot(
tic_plot,
"v",
markersize=10,
color="k",
label="selling signal",
markevery=selling_signal,
)
plt.title(
f"{df_trx_temp.name} Num Transactions: {len(buying_signal[buying_signal == True]) + len(selling_signal[selling_signal == True])}"
)
plt.legend()
plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=25))
plt.xticks(rotation=45, ha="right")
plt.show()