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plotting.py
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import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import json
from pure_funcs import round_dynamic, denumpyize, candidate_to_live_config
from njit_funcs import round_up
from procedures import dump_live_config
def dump_plots(result: dict, fdf: pd.DataFrame, df: pd.DataFrame):
plt.rcParams['figure.figsize'] = [29, 18]
pd.set_option('precision', 10)
def gain_conv(x):
return x * 100 - 100
lines = []
lines.append(f"exchange {result['exchange'] if 'exchange' in result else 'unknown'}")
lines.append(f"symbol {result['symbol'] if 'symbol' in result else 'unknown'}")
lines.append(f"gain percentage {round_dynamic(result['result']['gain'] * 100 - 100, 4)}%")
lines.append(f"average_daily_gain percentage {round_dynamic((result['result']['average_daily_gain'] - 1) * 100, 3)}%")
lines.append(f"sharp_gain_ratio percentage {round_dynamic(result['result']['sharp_gain_ratio'] * 100, 3)}%")
lines.append(f"closest_bkr percentage {round_dynamic(result['result']['closest_bkr'] * 100, 4)}%")
lines.append(f"starting balance {round_dynamic(result['starting_balance'], 3)}")
for key in [k for k in result['result'] if k not in ['gain', 'average_daily_gain', 'closest_bkr', 'do_long', 'do_shrt']]:
lines.append(f"{key} {round_dynamic(result['result'][key], 6)}")
lines.append(f"long: {result['do_long']}, short: {result['do_shrt']}")
longs = fdf[fdf.type.str.contains('long')]
shrts = fdf[fdf.type.str.contains('shrt')]
lines.append(f"n long ientries {len(longs[longs.type == 'long_ientry'])}")
lines.append(f"n long rentries {len(longs[longs.type == 'long_rentry'])}")
lines.append(f"n long ncloses {len(longs[longs.type == 'long_nclose'])}")
lines.append(f"n long scloses {len(longs[longs.type == 'long_sclose'])}")
lines.append(f"long pnl sum {longs.pnl.sum()}")
lines.append(f"n shrt ientries {len(shrts[shrts.type == 'shrt_ientry'])}")
lines.append(f"n shrt rentries {len(shrts[shrts.type == 'shrt_rentry'])}")
lines.append(f"n shrt ncloses {len(shrts[shrts.type == 'shrt_nclose'])}")
lines.append(f"n shrt scloses {len(shrts[shrts.type == 'shrt_sclose'])}")
lines.append(f"shrt pnl sum {shrts.pnl.sum()}")
live_config = candidate_to_live_config(result)
dump_live_config(live_config, result['plots_dirpath'] + 'live_config.json')
json.dump(denumpyize(result), open(result['plots_dirpath'] + 'result.json', 'w'), indent=4)
print('writing backtest_result.txt...')
with open(f"{result['plots_dirpath']}backtest_result.txt", 'w') as f:
for line in lines:
print(line)
f.write(line + '\n')
print('plotting balance and equity...')
plt.clf()
fdf.balance.plot()
fdf.equity.plot()
plt.savefig(f"{result['plots_dirpath']}balance_and_equity.png")
plt.clf()
longs.pnl.cumsum().plot()
plt.savefig(f"{result['plots_dirpath']}pnl_cumsum_long.png")
plt.clf()
shrts.pnl.cumsum().plot()
plt.savefig(f"{result['plots_dirpath']}pnl_cumsum_shrt.png")
plt.clf()
fdf.adg.plot()
plt.savefig(f"{result['plots_dirpath']}adg.png")
print('plotting backtest whole and in chunks...')
n_parts = max(3, int(round_up(result['n_days'] / 14, 1.0)))
for z in range(n_parts):
start_ = z / n_parts
end_ = (z + 1) / n_parts
print(f'{z} of {n_parts} {start_ * 100:.2f}% to {end_ * 100:.2f}%')
fig = plot_fills(df, fdf.iloc[int(len(fdf) * start_):int(len(fdf) * end_)], bkr_thr=0.1)
fig.savefig(f"{result['plots_dirpath']}backtest_{z + 1}of{n_parts}.png")
fig = plot_fills(df, fdf, bkr_thr=0.1)
fig.savefig(f"{result['plots_dirpath']}whole_backtest.png")
print('plotting pos sizes...')
plt.clf()
longs.psize.plot()
shrts.psize.plot()
plt.savefig(f"{result['plots_dirpath']}psizes_plot.png")
def plot_fills(df, fdf_, side: int = 0, bkr_thr=0.1):
plt.clf()
fdf = fdf_.set_index('timestamp')
dfc = df.iloc[::max(1, int(len(df) * 0.00001))]
dfc = dfc[(dfc.timestamp > fdf.index[0]) & (dfc.timestamp < fdf.index[-1])].set_index('timestamp')
dfc = dfc.loc[fdf.index[0]:fdf.index[-1]]
dfc.price.plot(style='y-')
if side >= 0:
longs = fdf[fdf.type.str.contains('long')]
lnentry = longs[(longs.type == 'long_ientry') | (longs.type == 'long_rentry')]
lhentry = longs[longs.type == 'long_hentry']
lnclose = longs[longs.type == 'long_nclose']
lsclose = longs[longs.type == 'long_sclose']
lnentry.price.plot(style='b.')
lhentry.price.plot(style='bx')
lnclose.price.plot(style='r.')
lsclose.price.plot(style=('rx'))
longs.where(longs.pprice != 0.0).pprice.fillna(method='ffill').plot(style='b--')
if side <= 0:
shrts = fdf[fdf.type.str.contains('shrt')]
snentry = shrts[(shrts.type == 'shrt_ientry') | (shrts.type == 'shrt_rentry')]
shentry = shrts[shrts.type == 'shrt_hentry']
snclose = shrts[shrts.type == 'shrt_nclose']
ssclose = shrts[shrts.type == 'shrt_sclose']
snentry.price.plot(style='r.')
shentry.price.plot(style='rx')
snclose.price.plot(style='b.')
ssclose.price.plot(style=('bx'))
shrts.where(shrts.pprice != 0.0).pprice.fillna(method='ffill').plot(style='r--')
if 'bkr_price' in fdf.columns:
fdf.bkr_price.where(fdf.bkr_diff < bkr_thr, np.nan).plot(style='k--')
return plt