-
-
Notifications
You must be signed in to change notification settings - Fork 1.1k
/
qbot_main.py
319 lines (265 loc) · 13.4 KB
/
qbot_main.py
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
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
'''
Author: Charmve [email protected]
Date: 2023-03-23 18:19:46
LastEditors: Charmve [email protected]
LastEditTime: 2023-06-09 23:41:01
FilePath: /Qbot/qbot_main.py
Version: 1.0.1
Blogs: charmve.blog.csdn.net
GitHub: https://github.com/Charmve
Description:
Copyright (c) 2023 by Charmve, All Rights Reserved.
Licensed under the MIT License.
'''
from email.policy import default
import os
import sys
import logging
import itertools
import tushare as ts
import talib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
logger = logging.getLogger(__name__)
# 指定默认字体
# matplotlib.use('QT5Agg')
mpl.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体为黑体
mpl.rcParams['axes.unicode_minus'] = False # 解决保存图像时负号'-'显示为方块的问题
plt.rcParams['font.family'] = ['Arial Unicode MS'] # 设置字体为 'Arial Unicode MS'
from tensorflow.keras.models import load_model
from qbot.strategies.lstm_strategy_bt import LSTMPredict
from utils.larkbot import LarkBot
import pync
top_path = os.path.dirname(os.path.abspath(sys.argv[0]))
sounds_file = os.path.join(top_path, "./qbot/asserts/statics/sounds/bell.wav")
def send_signal_sounds(type="buy"):
if type == "buy":
# if linux
# os.system('play ./qbot/asserts/statics/sounds/alert-bells.wav')
# if MacOs
os.system(f"afplay {sounds_file}")
elif type == "sell":
os.system(f"afplay {sounds_file}")
def send_signal_message_screen(symbol, price, type=default):
pync.notify(
f'{symbol}当前价格为{price}',
title=f'Qbot - {symbol}{type}',
open="https://ufund-me.github.io/",
appIcon="./qbot/gui/imgs/logo.ico",
)
# 设置股票代码
# stocks_pool = update_stocks()
stocks_pool = [
{"code": "sz000063", "name": "中兴通讯", "min_threshold": "26", "max_threshold": "38"},
{"code": "sh000016", "name": "上证50"},
{"coce": "601318", "name": "中国平安"},
]
# 设置股票代码和均线周期
# symbol = '600000.SH'
symbol = '600519'
ma_short = 5
ma_mid = 10
ma_long = 20
boll_period = 20
default_weights = {"BIAS": 0.1, "KDJ": 0.2, "RSI": 0.15, "BOLL": 0.25, "MACD": 0.2, "LSTM": 0.1}
broker_config = [{"setcash": 100000, "ballance": 100000, "stake": 100, "commission": 0.0005}]
# # 本金10000，每次交易100股
# broker.setcash(10000)
# broker.addsizer(stake=100)
# # 万五佣金
# broker.setcommission(commission=0.0005)
# WEBHOOK_SECRET = ""
# bot = LarkBot(secret=WEBHOOK_SECRET)
# 获取历史行情数据
data = ts.get_hist_data(symbol)
data = data.rename(columns={"pre_close" : "close"})
# 初始化图表
plt.ion()
# 标记买入卖出信号
# buy_signal.append([{"index": "20213213", "values": "1627.3", "strategy": "MACD", "symbol":"59991"}])
# buy_signal.append([{"index": "2012213", "values": "17.5", "strategy": "BOLL", "symbol":"15645"}])
buy_signal = []
sell_signal = []
def cal_fusion_result(signals):
result=0
print(signals)
for signal in signals:
# print("strategy: ", signal[0]["strategy"], "stock:", signal[0]["symbol"], default_weights[signal[0]["strategy"]])
result += default_weights[signal[0]["strategy"]]
# print("result:", result)
return result
def get_weights_distribution(data):
sums = [sum(combo) for combo in itertools.combinations(data.values(), 3)]
mean = sum(sums) / len(sums)
return mean
# 获取回测开始时的总资金
print("期初资金: %.2f" % broker_config[0]["setcash"])
while True:
# try:
if broker_config[0]["ballance"] < broker_config[0]["ballance"] * 0.16:
print("⚠️ 亏损大于 16%，停止交易程序。")
exit()
# 获取当前时刻的实时行情数据
# for ind, stock in enumerate(stocks_pool):
stock_data = ts.get_realtime_quotes(symbol)
latest_price = float(stock_data['price'].iloc[0])
stock_data = stock_data.rename(columns={"pre_close" : "close"})
stock_data["datetime"] = ''.join(stock_data["date"] + ' ' + stock_data["time"])
print(
"===> date_time: ", stock_data['datetime'].iloc[-1],
", code: ", stock_data['code'].iloc[-1],
", latest_price: ", stock_data['price'].iloc[-1])
# 将字符串类型的数据转换为float类型
for col in ['open', 'high', 'low', 'price', 'close']:
stock_data[col] = stock_data[col].astype(float)
# 将实时行情数据添加到历史行情数据中
# TEMP(ZHANGWEI): data = stock_data
data = pd.concat([data, stock_data])
# data = data.append(stock_data, ignore_index=True)
# 计算不同的技术指标
close_prices = data['close']
stack_name = data["name"][0]
print("=========================================================================================")
print("stack_name:", stack_name)
logging.debug("stack_name:", stack_name)
ma_short_data = talib.SMA(close_prices, timeperiod=ma_short)
ma_mid_data = talib.SMA(close_prices, timeperiod=ma_mid)
ma_long_data = talib.SMA(close_prices, timeperiod=ma_long)
print("5日、10日和20日均线: ", ma_short_data.iloc[-1], ma_mid_data.iloc[-1], ma_long_data.iloc[-1], close_prices.iloc[0])
# 背离率
bias1 = (close_prices - ma_short_data) / ma_short_data * 100
bias2 = (close_prices - ma_mid_data) / ma_mid_data * 100
bias3 = (close_prices - ma_long_data) / ma_long_data * 100
print(" BIAS指标: ", bias1.iloc[-1], bias2.iloc[-1], bias3.iloc[-1])
upper, middle, lower = talib.BBANDS(close_prices, timeperiod=boll_period)
print(" BOLL BANDS: ", upper.iloc[-1], middle.iloc[-1], lower.iloc[-1])
rsi = talib.RSI(close_prices, timeperiod=14)
print(" RSI指标: ", rsi.iloc[-1])
kdj_k, kdj_d = talib.STOCH(
data['high'],
data['low'],
data['close'],
fastk_period=9,
slowk_period=3,
slowd_period=3
)
kdj_j = 3 * kdj_k - 2 * kdj_d
print(" KDJ指标: ", kdj_k.iloc[-1], kdj_d.iloc[-1], kdj_j.iloc[-1])
# 计算MADC指标
macd, signal, hist = talib.MACD(data['close'])
# print("MACD:", macd)
print("=========================================================================================\n")
# 第一个策略：判断交易信号
signal = ''
# if(bias1 > bias2 and bias1 < bias2[-2]) \
# and (bias1 > bias3[-1] and bias1[-2] < bias3[-2]):
if (bias1.iloc[-1] > bias2.iloc[-1] and bias1.iloc[-1] > bias3.iloc[-1]) or (bias1.iloc[-1] == bias2.iloc[-1] and bias1.iloc[-1] == bias3.iloc[-1]):
signal = 'Buy Signal'
print(f"💡[{stack_name}] 股票bias1线上穿bias2和bias3：{latest_price}，买入信号")
# bot.send(content=f"💡 买入信号: {data['price'].iloc[-1]} 股票bias1线上穿bias2和bias3：. 当前价格 {latest_price}.")
send_signal_sounds(type="buy")
buy_signal.append([{"index": data['datetime'].iloc[-1], "values": latest_price, "strategy": "BIAS", "symbol": stack_name}])
elif (bias1.iloc[-1] < bias2.iloc[-1] and bias1.iloc[-1] < bias3.iloc[-1]):
# elif close_prices < lower:
signal = 'Sell Signal'
# bot.send(content=f"💡 ，卖出信号: {data['price'].iloc[-1]} 股票bias1线下穿bias2和bias3：. 当前价格 {latest_price}.")
print(f"💡[{stack_name}] 股票bias1线下穿bias2和bias3：{latest_price}，卖出信号")
sell_signal.append([{"index": data['datetime'].iloc[-1], "values": latest_price, "strategy": "BIAS", "symbol": stack_name}])
# 第二个策略：判断均线交叉情况并发出交易信号
if ma_short_data.iloc[-1] > ma_mid_data.iloc[-1] and ma_short_data.iloc[-1] > ma_long_data.iloc[-1]:
print(f"💡[{stack_name}] 股票价格上穿5日、10日和20日均线：{latest_price}，买入信号")
buy_signal.append([{"index": data['datetime'].iloc[-1], "values": latest_price, "strategy": "MACD", "symbol": stack_name}])
# 发出交易信号，例如发送邮件或短信等
send_signal_sounds(type="buy")
elif ma_short_data.iloc[-1] < ma_mid_data.iloc[-1] and ma_short_data.iloc[-1] < ma_long_data.iloc[-1]:
print(f"💡[{stack_name}] 股票价格下穿5日、10日和20日均线：{latest_price}，卖出信号")
sell_signal.append([{"index": data['datetime'].iloc[-1], "values": latest_price, "strategy": "MACD", "symbol": stack_name}])
# 发出交易信号，例如发送邮件或短信等
send_signal_sounds(type="sell")
send_signal_message_screen(stack_name, latest_price, type='')
# 第三个策略：K线上穿D线
if kdj_k.iloc[-1] > kdj_d.iloc[-1] and kdj_k.iloc[-1] < kdj_d.iloc[-1] and kdj_k.iloc[-1] < 80:
print(f"💡[{stack_name}] 股票K线上穿D线：{latest_price}，买入信号")
buy_signal.append([{"index": data['datetime'].iloc[-1], "values": latest_price, "strategy": "KDJ", "symbol": stack_name}])
# 发出交易信号，例如发送邮件或短信等
send_signal_sounds(type="buy")
elif kdj_k.iloc[-1] < kdj_d.iloc[-1] and kdj_k.iloc[-1] < kdj_d.iloc[-1]:
sell_signal.append([{"index": data['datetime'].iloc[-1], "values": latest_price, "strategy": "KDJ", "symbol": stack_name}])
print(f"💡[{stack_name}] 股票K线下穿D线：{latest_price}，卖出信号")
if rsi.iloc[-1] > 80:
buy_signal.append([{"index": data['datetime'].iloc[-1], "values": latest_price, "strategy": "RSI", "symbol": stack_name}])
print(f"💡[{stack_name}] 股票趋势指标RSI大于 80：{latest_price}，买入信号")
send_signal_sounds(type="buy")
elif rsi.iloc[-1] < 20:
sell_signal.append([{"index": data['datetime'].iloc[-1], "values": latest_price, "strategy": "RSI", "symbol": stack_name}])
send_signal_sounds(type="sell")
print(f"💡[{stack_name}] 股票趋势指标RSI小于 20：{latest_price}，卖出信号")
# 第四个策略：判断价格是否低于BOLL底
if latest_price < lower.iloc[-1]:
print(f"💡[{stack_name}] 股票价格低于BOLL底：{latest_price} < {lower.iloc[-1]}")
buy_signal.append([{"index": data['datetime'].iloc[-1], "values": latest_price, "strategy": "BOLL", "symbol": stack_name}])
# 发出交易信号，例如发送邮件或短信等
send_signal_sounds(type="buy")
elif latest_price > upper.iloc[-1]:
print(f"💡[{stack_name}] 股票价格高于BOLL顶：{latest_price} > {upper.iloc[-1]}")
sell_signal.append([{"index": data['datetime'].iloc[-1], "values": latest_price, "strategy": "BOLL", "symbol": stack_name}])
# 发出交易信号，例如发送邮件或短信等
send_signal_sounds(type="sell")
else:
print("Holding, no trade.")
# 第五个策略：判断价格是否低于LSTM预测价格
# 计算LSTM预测价格
lstm_model = load_model('./qbot/lstm_model.h5')
predic_price = lstm_model.predict(np.array([latest_price]))
# predic_price = 10
if latest_price < predic_price:
print(f"💡[{stack_name}] 股票价格低于预测价格：{latest_price} < {predic_price}")
buy_signal.append([{"index": data['datetime'].iloc[-1], "values": latest_price, "strategy": "LSTM", "symbol": stack_name}])
# 发出交易信号，例如发送邮件或短信等
send_signal_sounds(type="buy")
# 最终决策：
print("\n\n\nFusion Result:")
if cal_fusion_result(buy_signal) > get_weights_distribution(default_weights):
print(f"BUY. {stack_name}, 500 stocks, {data['datetime'].iloc[-1]}")
elif cal_fusion_result(sell_signal) > get_weights_distribution(default_weights):
print(f"SELL. {stack_name}, 500 stocks, {data['datetime'].iloc[-1]}")
else:
print("HOLDING, NO HANDLE ...")
# 绘制实时数据图
plt.clf()
plt.plot(data['close'].iloc[-1], label='Close')
# 画出5日均线、10日均线和20日均线图
plt.plot(ma_short_data.iloc[-1], label='MA5')
plt.plot(ma_mid_data.iloc[-1], label='MA10')
plt.plot(ma_long_data.iloc[-1], label='MA20')
# 标记买入卖出信号
for buy_sig in buy_signal:
plt.plot(buy_sig[0]["index"], str(buy_sig[0]["values"]), '^', markersize=8, color='green', label='Buy Signal')
break
for sell_sig in sell_signal:
plt.plot(sell_sig[0]["index"], str(sell_sig[0]["values"]), 'v', markersize=8, color='red', label='Sell Signal')
break
plt.plot(bias1.iloc[-1], label='Bias1')
plt.plot(bias2.iloc[-1], label='Bias2')
plt.plot(bias3.iloc[-1], label='Bias3')
plt.plot(upper.iloc[-1], label='Upper')
plt.plot(middle.iloc[-1], label='Middle')
plt.plot(lower.iloc[-1], label='Lower')
plt.plot(rsi.iloc[-1], label='RSI')
plt.plot(kdj_k.iloc[-1], label='KDJ_K')
plt.plot(kdj_d.iloc[-1], label='KDJ_D')
# 添加图例和标题
# plt.title('2020东京奥运会金牌数分布')
# plt.title(f'Real-time Stock Price Monitoring [{data["name"][0]} ({symbol})]', fontproperties='YaHei')
# plt.legend(loc='upper left')
plt.title(f'Real-time Stock Price Monitoring [{data["name"][0]} ({symbol})]')
plt.legend(loc='best')
plt.draw()
plt.pause(10)
# # 计算每日收益
# broker_config[0]["ballance"] = broker_config[0]["ballance"] + benefits - broker_config[0]s["stake"] * trade_times * broker_config["commission"]
# print('Sharpe Ratio:', thestrat.analyzers.mysharpe.get_analysis()['sharperatio'])
# except Exception as e:
# print('Error:', e)