forked from neurotrader888/TechnicalAnalysisAutomation
/
test_flag_patterns.py
263 lines (229 loc) · 10.3 KB
/
test_flag_patterns.py
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import mplfinance as mpf
from flags_pennants import find_flags_pennants_pips, find_flags_pennants_trendline
data = pd.read_csv('BTCUSDT3600.csv')
data['date'] = data['date'].astype('datetime64[s]')
data = data.set_index('date')
data = np.log(data)
dat_slice = data['close'].to_numpy()
orders = list(range(3, 49))
bull_flag_wr = []
bull_pennant_wr = []
bear_flag_wr = []
bear_pennant_wr = []
bull_flag_avg = []
bull_pennant_avg = []
bear_flag_avg = []
bear_pennant_avg = []
bull_flag_count = []
bull_pennant_count = []
bear_flag_count = []
bear_pennant_count = []
bull_flag_total_ret = []
bull_pennant_total_ret = []
bear_flag_total_ret = []
bear_pennant_total_ret = []
for order in orders:
bull_flags, bear_flags, bull_pennants, bear_pennants = find_flags_pennants_pips(dat_slice, order)
#bull_flags, bear_flags, bull_pennants, bear_pennants = find_flags_pennants_trendline(dat_slice, order)
bull_flag_df = pd.DataFrame()
bull_pennant_df = pd.DataFrame()
bear_flag_df = pd.DataFrame()
bear_pennant_df = pd.DataFrame()
# Assemble data into dataframe
hold_mult = 1.0 # Multipler of flag width to hold for after a pattern
for i, flag in enumerate(bull_flags):
bull_flag_df.loc[i, 'flag_width'] = flag.flag_width
bull_flag_df.loc[i, 'flag_height'] = flag.flag_height
bull_flag_df.loc[i, 'pole_width'] = flag.pole_width
bull_flag_df.loc[i, 'pole_height'] = flag.pole_height
bull_flag_df.loc[i, 'slope'] = flag.resist_slope
hp = int(flag.flag_width * hold_mult)
if flag.conf_x + hp >= len(data):
bull_flag_df.loc[i, 'return'] = np.nan
else:
ret = dat_slice[flag.conf_x + hp] - dat_slice[flag.conf_x]
bull_flag_df.loc[i, 'return'] = ret
for i, flag in enumerate(bear_flags):
bear_flag_df.loc[i, 'flag_width'] = flag.flag_width
bear_flag_df.loc[i, 'flag_height'] = flag.flag_height
bear_flag_df.loc[i, 'pole_width'] = flag.pole_width
bear_flag_df.loc[i, 'pole_height'] = flag.pole_height
bear_flag_df.loc[i, 'slope'] = flag.support_slope
hp = int(flag.flag_width * hold_mult)
if flag.conf_x + hp >= len(data):
bear_flag_df.loc[i, 'return'] = np.nan
else:
ret = -1 * (dat_slice[flag.conf_x + hp] - dat_slice[flag.conf_x])
bear_flag_df.loc[i, 'return'] = ret
for i, pennant in enumerate(bull_pennants):
bull_pennant_df.loc[i, 'pennant_width'] = pennant.flag_width
bull_pennant_df.loc[i, 'pennant_height'] = pennant.flag_height
bull_pennant_df.loc[i, 'pole_width'] = pennant.pole_width
bull_pennant_df.loc[i, 'pole_height'] = pennant.pole_height
hp = int(pennant.flag_width * hold_mult)
if pennant.conf_x + hp >= len(data):
bull_pennant_df.loc[i, 'return'] = np.nan
else:
ret = dat_slice[pennant.conf_x + hp] - dat_slice[pennant.conf_x]
bull_pennant_df.loc[i, 'return'] = ret
for i, pennant in enumerate(bear_pennants):
bear_pennant_df.loc[i, 'pennant_width'] = pennant.flag_width
bear_pennant_df.loc[i, 'pennant_height'] = pennant.flag_height
bear_pennant_df.loc[i, 'pole_width'] = pennant.pole_width
bear_pennant_df.loc[i, 'pole_height'] = pennant.pole_height
hp = int(pennant.flag_width * hold_mult)
if pennant.conf_x + hp >= len(data):
bear_pennant_df.loc[i, 'return'] = np.nan
else:
ret = -1 * (dat_slice[pennant.conf_x + hp] - dat_slice[pennant.conf_x])
bear_pennant_df.loc[i, 'return'] = ret
if len(bull_flag_df) > 0:
bull_flag_count.append(len(bull_flag_df))
bull_flag_avg.append(bull_flag_df['return'].mean())
bull_flag_wr.append(len(bull_flag_df[bull_flag_df['return'] > 0]) / len(bull_flag_df))
bull_flag_total_ret.append(bull_flag_df['return'].sum())
else:
bull_flag_count.append(0)
bull_flag_avg.append(np.nan)
bull_flag_wr.append(np.nan)
bull_flag_total_ret.append(0)
if len(bear_flag_df) > 0:
bear_flag_count.append(len(bear_flag_df))
bear_flag_avg.append(bear_flag_df['return'].mean())
bear_flag_wr.append(len(bear_flag_df[bear_flag_df['return'] > 0]) / len(bear_flag_df))
bear_flag_total_ret.append(bear_flag_df['return'].sum())
else:
bear_flag_count.append(0)
bear_flag_avg.append(np.nan)
bear_flag_wr.append(np.nan)
bear_flag_total_ret.append(0)
if len(bull_pennant_df) > 0:
bull_pennant_count.append(len(bull_pennant_df))
bull_pennant_avg.append(bull_pennant_df['return'].mean())
bull_pennant_wr.append(len(bull_pennant_df[bull_pennant_df['return'] > 0]) / len(bull_pennant_df))
bull_pennant_total_ret.append(bull_pennant_df['return'].sum())
else:
bull_pennant_count.append(0)
bull_pennant_avg.append(np.nan)
bull_pennant_wr.append(np.nan)
bull_pennant_total_ret.append(0)
if len(bear_pennant_df) > 0:
bear_pennant_count.append(len(bear_pennant_df))
bear_pennant_avg.append(bear_pennant_df['return'].mean())
bear_pennant_wr.append(len(bear_pennant_df[bear_pennant_df['return'] > 0]) / len(bear_pennant_df))
bear_pennant_total_ret.append(bear_pennant_df['return'].sum())
else:
bear_pennant_count.append(0)
bear_pennant_avg.append(np.nan)
bear_pennant_wr.append(np.nan)
bear_pennant_total_ret.append(0)
results_df = pd.DataFrame(index=orders)
results_df['bull_flag_count'] = bull_flag_count
results_df['bull_flag_avg'] = bull_flag_avg
results_df['bull_flag_wr'] = bull_flag_wr
results_df['bull_flag_total'] = bull_flag_total_ret
results_df['bear_flag_count'] = bear_flag_count
results_df['bear_flag_avg'] = bear_flag_avg
results_df['bear_flag_wr'] = bear_flag_wr
results_df['bear_flag_total'] = bear_flag_total_ret
results_df['bull_pennant_count'] = bull_pennant_count
results_df['bull_pennant_avg'] = bull_pennant_avg
results_df['bull_pennant_wr'] = bull_pennant_wr
results_df['bull_pennant_total'] = bull_pennant_total_ret
results_df['bear_pennant_count'] = bear_pennant_count
results_df['bear_pennant_avg'] = bear_pennant_avg
results_df['bear_pennant_wr'] = bear_pennant_wr
results_df['bear_pennant_total'] = bear_pennant_total_ret
# Plot bull flag results
plt.style.use('dark_background')
fig, ax = plt.subplots(2, 2)
fig.suptitle("Bull Flag Performance", fontsize=20)
results_df['bull_flag_count'].plot.bar(ax=ax[0,0])
results_df['bull_flag_avg'].plot.bar(ax=ax[0,1], color='yellow')
results_df['bull_flag_total'].plot.bar(ax=ax[1,0], color='green')
results_df['bull_flag_wr'].plot.bar(ax=ax[1,1], color='orange')
ax[0,1].hlines(0.0, xmin=-1, xmax=len(orders), color='white')
ax[1,0].hlines(0.0, xmin=-1, xmax=len(orders), color='white')
ax[1,1].hlines(0.5, xmin=-1, xmax=len(orders), color='white')
ax[0,0].set_title('Number of Patterns Found')
ax[0,0].set_xlabel('Order Parameter')
ax[0,0].set_ylabel('Number of Patterns')
ax[0,1].set_title('Average Pattern Return')
ax[0,1].set_xlabel('Order Parameter')
ax[0,1].set_ylabel('Average Log Return')
ax[1,0].set_title('Sum of Returns')
ax[1,0].set_xlabel('Order Parameter')
ax[1,0].set_ylabel('Total Log Return')
ax[1,1].set_title('Win Rate')
ax[1,1].set_xlabel('Order Parameter')
ax[1,1].set_ylabel('Win Rate Percentage')
plt.show()
fig, ax = plt.subplots(2, 2)
fig.suptitle("Bear Flag Performance", fontsize=20)
results_df['bear_flag_count'].plot.bar(ax=ax[0,0])
results_df['bear_flag_avg'].plot.bar(ax=ax[0,1], color='yellow')
results_df['bear_flag_total'].plot.bar(ax=ax[1,0], color='green')
results_df['bear_flag_wr'].plot.bar(ax=ax[1,1], color='orange')
ax[0,1].hlines(0.0, xmin=-1, xmax=len(orders), color='white')
ax[1,0].hlines(0.0, xmin=-1, xmax=len(orders), color='white')
ax[1,1].hlines(0.5, xmin=-1, xmax=len(orders), color='white')
ax[0,0].set_title('Number of Patterns Found')
ax[0,0].set_xlabel('Order Parameter')
ax[0,0].set_ylabel('Number of Patterns')
ax[0,1].set_title('Average Pattern Return')
ax[0,1].set_xlabel('Order Parameter')
ax[0,1].set_ylabel('Average Log Return')
ax[1,0].set_title('Sum of Returns')
ax[1,0].set_xlabel('Order Parameter')
ax[1,0].set_ylabel('Total Log Return')
ax[1,1].set_title('Win Rate')
ax[1,1].set_xlabel('Order Parameter')
ax[1,1].set_ylabel('Win Rate Percentage')
plt.show()
fig, ax = plt.subplots(2, 2)
fig.suptitle("Bull Pennant Performance", fontsize=20)
results_df['bull_pennant_count'].plot.bar(ax=ax[0,0])
results_df['bull_pennant_avg'].plot.bar(ax=ax[0,1], color='yellow')
results_df['bull_pennant_total'].plot.bar(ax=ax[1,0], color='green')
results_df['bull_pennant_wr'].plot.bar(ax=ax[1,1], color='orange')
ax[0,1].hlines(0.0, xmin=-1, xmax=len(orders), color='white')
ax[1,0].hlines(0.0, xmin=-1, xmax=len(orders), color='white')
ax[1,1].hlines(0.5, xmin=-1, xmax=len(orders), color='white')
ax[0,0].set_title('Number of Patterns Found')
ax[0,0].set_xlabel('Order Parameter')
ax[0,0].set_ylabel('Number of Patterns')
ax[0,1].set_title('Average Pattern Return')
ax[0,1].set_xlabel('Order Parameter')
ax[0,1].set_ylabel('Average Log Return')
ax[1,0].set_title('Sum of Returns')
ax[1,0].set_xlabel('Order Parameter')
ax[1,0].set_ylabel('Total Log Return')
ax[1,1].set_title('Win Rate')
ax[1,1].set_xlabel('Order Parameter')
ax[1,1].set_ylabel('Win Rate Percentage')
plt.show()
fig, ax = plt.subplots(2, 2)
fig.suptitle("Bear Pennant Performance", fontsize=20)
results_df['bear_pennant_count'].plot.bar(ax=ax[0,0])
results_df['bear_pennant_avg'].plot.bar(ax=ax[0,1], color='yellow')
results_df['bear_pennant_total'].plot.bar(ax=ax[1,0], color='green')
results_df['bear_pennant_wr'].plot.bar(ax=ax[1,1], color='orange')
ax[0,1].hlines(0.0, xmin=-1, xmax=len(orders), color='white')
ax[1,0].hlines(0.0, xmin=-1, xmax=len(orders), color='white')
ax[1,1].hlines(0.5, xmin=-1, xmax=len(orders), color='white')
ax[0,0].set_title('Number of Patterns Found')
ax[0,0].set_xlabel('Order Parameter')
ax[0,0].set_ylabel('Number of Patterns')
ax[0,1].set_title('Average Pattern Return')
ax[0,1].set_xlabel('Order Parameter')
ax[0,1].set_ylabel('Average Log Return')
ax[1,0].set_title('Sum of Returns')
ax[1,0].set_xlabel('Order Parameter')
ax[1,0].set_ylabel('Total Log Return')
ax[1,1].set_title('Win Rate')
ax[1,1].set_xlabel('Order Parameter')
ax[1,1].set_ylabel('Win Rate Percentage')
plt.show()