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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
from prettytable import PrettyTable
from colorama import init, Fore
def dump_plots(result: dict, fdf: pd.DataFrame, df: pd.DataFrame):
init(autoreset=True)
plt.rcParams['figure.figsize'] = [29, 18]
pd.set_option('precision', 10)
table = PrettyTable(["Metric", "Value"])
table.align['Metric'] = 'l'
table.align['Value'] = 'l'
table.title = 'Summary'
table.add_row(['Exchange', result['exchange'] if 'exchange' in result else 'unknown'])
table.add_row(['Market type', result['market_type'] if 'market_type' in result else 'unknown'])
table.add_row(['Symbol', result['symbol'] if 'symbol' in result else 'unknown'])
table.add_row(['No. days', round_dynamic(result['result']['n_days'], 6)])
table.add_row(['Starting balance', round_dynamic(result['result']['starting_balance'], 6)])
profit_color = Fore.RED if result['result']['final_balance'] < result['result']['starting_balance'] else Fore.RESET
table.add_row(['Final balance', f"{profit_color}{round_dynamic(result['result']['final_balance'], 6)}{Fore.RESET}"])
table.add_row(['Final equity', f"{profit_color}{round_dynamic(result['result']['final_equity'], 6)}{Fore.RESET}"])
table.add_row(['Net PNL + fees', f"{profit_color}{round_dynamic(result['result']['net_pnl_plus_fees'], 6)}{Fore.RESET}"])
table.add_row(['Total gain percentage', f"{profit_color}{round_dynamic(result['result']['gain'] * 100 - 100, 4)}%{Fore.RESET}"])
table.add_row(['Average daily gain percentage', f"{profit_color}{round_dynamic((result['result']['average_daily_gain'] - 1) * 100, 3)}%{Fore.RESET}"])
table.add_row(['Adjusted daily gain', f"{profit_color}{round_dynamic(result['result']['adjusted_daily_gain'], 6)}{Fore.RESET}"])
table.add_row([f"{result['avg_periodic_gain_key']} percentage", f"{profit_color}{round_dynamic(result['result']['average_periodic_gain'] * 100, 3)}%{Fore.RESET}"])
bankruptcy_color = Fore.RED if result['result']['closest_bkr'] < 0.4 else Fore.YELLOW if result['result']['closest_bkr'] < 0.8 else Fore.RESET
table.add_row(['Closest bankruptcy percentage', f"{bankruptcy_color}{round_dynamic(result['result']['closest_bkr'] * 100, 4)}%{Fore.RESET}"])
table.add_row([' ', ' '])
table.add_row(['Sharpe ratio', round_dynamic(result['result']['sharpe_ratio'], 6)])
table.add_row(['Profit sum', f"{profit_color}{round_dynamic(result['result']['profit_sum'], 6)}{Fore.RESET}"])
table.add_row(['Loss sum', f"{Fore.RED}{round_dynamic(result['result']['loss_sum'], 6)}{Fore.RESET}"])
table.add_row(['Fee sum', round_dynamic(result['result']['fee_sum'], 6)])
table.add_row(['Lowest equity/balance ratio', round_dynamic(result['result']['lowest_eqbal_ratio'], 6)])
table.add_row(['Biggest position size', round_dynamic(result['result']['biggest_psize'], 6)])
table.add_row([' ', ' '])
table.add_row(['No. fills', round_dynamic(result['result']['n_fills'], 6)])
table.add_row(['No. entries', round_dynamic(result['result']['n_entries'], 6)])
table.add_row(['No. closes', round_dynamic(result['result']['n_closes'], 6)])
table.add_row(['No. initial entries', round_dynamic(result['result']['n_initial_entries'], 6)])
table.add_row(['No. reentries', round_dynamic(result['result']['n_reentries'], 6)])
table.add_row(['No. normal closes', round_dynamic(result['result']['n_normal_closes'], 6)])
table.add_row(['No. stoploss closes', round_dynamic(result['result']['n_stop_loss_closes'], 6)])
table.add_row([' ', ' '])
table.add_row(['Mean hours between fills', round_dynamic(result['result']['mean_hrs_between_fills'], 6)])
table.add_row(['Max hours no fills (same side)', round_dynamic(result['result']['max_hrs_no_fills_same_side'], 6)])
table.add_row(['Max hours no fills', round_dynamic(result['result']['max_hrs_no_fills'], 6)])
longs = fdf[fdf.type.str.contains('long')]
shrts = fdf[fdf.type.str.contains('shrt')]
if result['do_long']:
table.add_row([' ', ' '])
table.add_row(['Long', result['do_long']])
table.add_row(["No. inital entries", len(longs[longs.type.str.contains('long_ientry')])])
table.add_row(["No. reentries", len(longs[longs.type.str.contains('long_rentry')])])
table.add_row(["No. normal closes", len(longs[longs.type.str.contains('long_nclose')])])
table.add_row(["No. stoploss closes", len(longs[longs.type.str.contains('long_sclose')])])
table.add_row(["No. partial fills", len(longs[longs.type.str.contains('partial')])])
table.add_row(['Mean hours between fills (long)', round_dynamic(result['result']['mean_hrs_between_fills_long'], 6)])
table.add_row(['Max hours no fills (long)', round_dynamic(result['result']['max_hrs_no_fills_long'], 6)])
profit_color = Fore.RED if longs.pnl.sum() < 0 else Fore.RESET
table.add_row(["PNL sum", f"{profit_color}{longs.pnl.sum()}{Fore.RESET}"])
if result['do_shrt']:
table.add_row([' ', ' '])
table.add_row(['Short', result['do_shrt']])
table.add_row(["No. initial entries", len(shrts[shrts.type.str.contains('shrt_ientry')])])
table.add_row(["No. reentries", len(shrts[shrts.type.str.contains('shrt_rentry')])])
table.add_row(["No. normal closes", len(shrts[shrts.type.str.contains('shrt_nclose')])])
table.add_row(["No. stoploss closes", len(shrts[shrts.type.str.contains('shrt_sclose')])])
table.add_row(["No. partial fills", len(shrts[shrts.type.str.contains('partial')])])
table.add_row(['Mean hours between fills (short)', round_dynamic(result['result']['mean_hrs_between_fills_shrt'], 6)])
table.add_row(['Max hours no fills (short)', round_dynamic(result['result']['max_hrs_no_fills_shrt'], 6)])
profit_color = Fore.RED if shrts.pnl.sum() < 0 else Fore.RESET
table.add_row(["PNL sum", f"{profit_color}{shrts.pnl.sum()}{Fore.RESET}"])
dump_live_config(result, result['plots_dirpath'] + 'live_config.json')
json.dump(denumpyize(result), open(result['plots_dirpath'] + 'result.json', 'w'), indent=4)
print('writing backtest_result.txt...\n')
with open(f"{result['plots_dirpath']}backtest_result.txt", 'w') as f:
output = table.get_string(border=True, padding_width=1)
print(output)
f.write(output)
print('\nplotting 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)
if fig is not None:
fig.savefig(f"{result['plots_dirpath']}backtest_{z + 1}of{n_parts}.png")
else:
print('no fills...')
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):
if fdf_.empty:
return
plt.clf()
fdf = fdf_.set_index('timestamp')
dfc = df.iloc[::max(1, int(len(df) * 0.00001))]
if dfc.index.name != 'timestamp':
dfc = dfc.set_index('timestamp')
dfc = dfc[(dfc.index > fdf.index[0]) & (dfc.index < fdf.index[-1])]
dfc = dfc.loc[fdf.index[0]:fdf.index[-1]]
dfc.price.plot(style='y-')
if side >= 0:
longs = fdf[fdf.type.str.contains('long')]
lientry = longs[longs.type.str.contains('long_ientry')]
lrentry = longs[longs.type.str.contains('long_rentry')]
lnclose = longs[longs.type.str.contains('long_nclose')]
lsclose = longs[longs.type.str.contains('long_sclose')]
lientry.price.plot(style='b.')
lrentry.price.plot(style='b.')
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')]
sientry = shrts[shrts.type.str.contains('shrt_ientry')]
srentry = shrts[shrts.type.str.contains('shrt_rentry')]
snclose = shrts[shrts.type.str.contains('shrt_nclose')]
ssclose = shrts[shrts.type.str.contains('shrt_sclose')]
sientry.price.plot(style='r.')
srentry.price.plot(style='r.')
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