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8ddd02c 8dc00a3 8ddd02c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 | import io
import os
import json
import requests
import pandas as pd
import mplfinance as mpf
import matplotlib.pyplot as plt
from PIL import Image
def generate_pil_img():
img_buf = io.BytesIO()
plt.savefig(img_buf, format='png')
return Image.open(img_buf)
def plot_Klines_fig(bars, code, precision=0.0001):
fig, axes = mpf.plot(bars, type='candle', mav=(5, 10), volume=True, title=f'\n{code}',
figsize=(12, 6), returnfig=True)
fixed_txt = 'Predict\nNext Bar'
for ci in ['Open', 'High', 'Low', 'Close', 'Volume']:
fixed_txt += f'\n{ci[0]}:{"{:.3e}".format(bars.iloc[-1][ci])}'
hl_chg = bars['High'].iloc[-1] / bars['Low'].iloc[-1] - 1.0
axes[0].annotate(fixed_txt, textcoords='axes fraction', xytext=(1.006, -0.37), color='r',
bbox=dict(facecolor='gray'),
xy=(len(bars) - 1, max(bars['Low'].min(), bars.iloc[-1]['Low'] * (1 - hl_chg / 4))),
arrowprops=dict(facecolor='r', width=5))
return generate_pil_img()
def plot_txt(text):
plt.figure(figsize=(12, 6))
plt.text(0.2, 0.8, text, size=12, alpha=0.8)
plt.axis('off')
return generate_pil_img()
def Kline_predict_plot(model_id, symbols, user_opinion, frequency, feature_saturation, sensitivity,
latest_bar_completed, show_bars, once_max):
params = {
'codes_str': symbols,
'user_opinion': user_opinion,
'frequency': frequency,
'feature_saturation': feature_saturation,
'latest_completed': latest_bar_completed,
'sensitivity': sensitivity,
'show_bars': show_bars,
'once_max': once_max,
}
headers = {'Content-Type': 'application/json'}
response = requests.post(os.environ[model_id] + 'predict', data=json.dumps(params), headers=headers)
result = response.json()
coins_not_available = result['codes_remove']
klines_data = result['klines_data']
figs = []
for codei, vi in klines_data.items():
bars = pd.DataFrame(vi)
bars.index = bars.index.map(pd.Timestamp)
figs.append(plot_Klines_fig(bars, codei))
if len(figs) < 1:
figs = [plot_txt('No available symbols in `Symbols` box.')]
return ','.join(coins_not_available) if len(coins_not_available) > 0 else 'No one', figs
def back_test_plot(model_id, symbols, frequency, feature_saturation, sensitivity, test_bars):
params = {
'codes_str': symbols,
'frequency': frequency,
'feature_saturation': feature_saturation,
'sensitivity': sensitivity,
'test_bars': test_bars,
}
headers = {'Content-Type': 'application/json'}
response = requests.post(os.environ[model_id] + 'back_test', data=json.dumps(params), headers=headers)
result = response.json()
test_result = result['test_result']
if isinstance(test_result, str):
return [plot_txt(test_result)]
else:
assert isinstance(test_result, dict), "`test_result` type is wrong."
keys_list = list(test_result.keys())
code, code_name = keys_list if len(keys_list[0]) < len(keys_list[1]) else keys_list[::-1]
labels, profits = pd.Series(test_result[code]), pd.Series(test_result[code_name])
labels.index, profits.index = labels.index.map(pd.Timestamp), profits.index.map(pd.Timestamp)
plt.figure()
(profits + 1.0).cumprod().plot(
label=f'profits => win_ratio:{round((profits > 0).mean() * 100, 2)}%')
(labels + 1.0).cumprod().plot(
label=f'{code} => up_ratio:{round((labels > 0).mean() * 100, 2)}%', linestyle='--')
plt.title(code_name)
plt.legend()
plt.grid()
return [generate_pil_img()]
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