def calculate_signals(self):
self.over_ema12 = []
self.over_ema20 = []
self.over_ama = []
self.ama_direction = []
self.svm_probability = []
for i in range(len(self.etfs)):
# 用weekly数据计算ema12
data = load_rawdata(self.etfs[i], 'weekly')
data_stats = stockstats.StockDataFrame.retype(data.copy())
self.over_ema12.append(data_stats['close_12_ema'].iloc[-1])
# 用weekly数据计算ama
# # 首先创建ama的模型数据
# data = load_ama_modeldata(self.etfs[i], 'weekly')
# input = np.array(data)[-2:, 0:-1]
#
# # 添加ama_direction
# self.ama_direction.append(input[-1, 1])
#
# # 加载模型
# file = open('./models/ama_models/' + self.etfs[i] + '_weekly_ama_model', 'rb')
# ama_model = pickle.load(file)
# # 计算ama
# self.over_ama.append(ama_model.predict(input)[-1])
# file.close()
ama, ama_direction, _ = calculate_ama(
data, data_stats, self.model_parameters['AMA']['price_type'],
self.model_parameters['AMA']['er_window'],
self.model_parameters['AMA']['slow_window'],
self.model_parameters['AMA']['fast_window'])
self.over_ama.append(ama[-1])
self.ama_direction.append(ama_direction[-1])
# 用weekly数据计算svm
# 首先创建svm需要的数据
data = load_svm_modeldata(self.etfs[i], 'weekly')
if len(data) > 300:
X, Y = preprocess(data.iloc[-301:-2])
else:
X, Y = preprocess(data)
input = X[-2:]
# 加载模型
file = open(
'./models/svm_models/' + self.etfs[i] + '_weekly_svm_model',
'rb')
svm_model = pickle.load(file)
self.svm_probability.append(svm_model.predict_proba(input)[-1, 1])
file.close()
# 用月数据计算ema20
data = load_rawdata(self.etfs[i], 'monthly')
data_stats = stockstats.StockDataFrame.retype(data.copy())
self.over_ema20.append(data_stats['close_20_ema'].iloc[-1])
self.show_signals()
def __init__(self,
etf,
start_date,
end_date,
model_name='AMA',
initial_deposit=100000,
price_type='open',
er_window=9,
slow_window=20,
fast_window=4):
self.ticker = etf
self.start_date = start_date
self.end_date = end_date
self.model_name = model_name
self.init_deposit = initial_deposit
self.price_type = price_type
self.er_window = er_window
self.slow_window = slow_window
self.fast_window = fast_window
self.raw_data = load_rawdata(etf, 'weekly')
self.indicators = stockstats.StockDataFrame.retype(
self.raw_data.copy())
ama, _, _ = calculate_ama(self.raw_data, self.indicators,
self.price_type, self.er_window,
self.slow_window, self.fast_window)
self.raw_data['ama'] = ama
self.report = pd.DataFrame(
columns=['position size', 'total', 'profit'])
def build_model_data(self, ticker, time_frame):
raw_data = load_rawdata(ticker, time_frame)
data_stats = stockstats.StockDataFrame.retype(raw_data.copy())
columns = ['ama', 'pn_er', 'tr', 'target']
ama, pn_er, ssc = calculate_ama(raw_data, data_stats, self.price_type, self.er_window, self.slow_window, self.fast_window)
tr = np.array(data_stats['tr'])
tr[0] = abs(raw_data.iloc[0]['open'] - raw_data.iloc[0]['close'])
# 计算target
target = []
price_open = np.array(raw_data['open'])
price_close = np.array(raw_data['close'])
target.append(price_close[0])
for i in range(1, len(price_open)):
if pn_er[i] > -0.1:
target.append(min(price_close[i], price_open[i]) - pn_er[i] * tr[i])
else:
target.append(max(price_close[i], price_open[i] - 0.3 * pn_er[i] * tr[i]))
#对ama曲线进行指数平滑， 窗口取7
if time_frame == 'weekly':
for i in range(1, len(target)):
target[i] = (2 * target[i] + 6 * target[i-1])/8
else:
for i in range(1, len(target)):
target[i] = (2 * target[i] + 9 * target[i-1])/11
model_data = np.array([ama, pn_er, tr, target]).T
print(model_data.shape)
model_data = pd.DataFrame(model_data, index=raw_data.index, columns=columns)
self.save_model_data(model_data, ticker, time_frame)
def __init__(self, etf, start_date, end_date, model_name='SVM',initial_deposit=100000):
self.ticker = etf
self.start_date = start_date
self.end_date = end_date
self.model_name = model_name
self.init_deposit = initial_deposit
self.raw_data = load_rawdata(etf, 'weekly')
self.indicators = stockstats.StockDataFrame.retype(self.raw_data.copy())
self.model_data = load_svm_modeldata(etf, 'weekly')
self.report = pd.DataFrame(columns=['position size','total','profit'])
def prepare_data(self):
# 获取被选中的etf名称和time frame
etf = self.chosen_etf.text()
tf = self.chosen_tf.text()
indicators = self.chosen_indicator.text().split(';')
_ = indicators.pop()
# load data
self.data = load_rawdata(etf, tf)
self.data_stats = stockstats.StockDataFrame.retype(self.data.copy())
# 去掉volume和adj close列
self.data.drop(['adjclose', 'volume'], axis=1, inplace=True)
# 添加所指标ama, ema12, ema20
ama, _, _ = calculate_ama(self.data, self.data_stats)
self.data['ama'] = ama
self.data[['ema12', 'ema20'
]] = self.data_stats[['close_12_ema', 'close_20_ema']]
# 数值保留两位小数
self.data[['open', 'high', 'low', 'close', 'ama', 'ema12',
'ema20']] = self.data[[
'open', 'high', 'low', 'close', 'ama', 'ema12', 'ema20'
]].round(decimals=2)
# 设置价格显示面板
# 获取四个价
self.current_price = self.data.iloc[-1]['close']
self.open_price = self.data.iloc[-1]['open']
self.high_price = self.data.iloc[-1]['high']
self.low_price = self.data.iloc[-1]['low']
self.current_label.setText('Current: ' + str(self.current_price))
if self.current_price > self.open_price:
self.current_label.setStyleSheet("color:green")
self.current_label.setFont(self.myfont)
else:
self.current_label.setStyleSheet("color:red")
self.current_label.setFont(self.myfont)
self.open_label.setText('Open: ' + str(self.open_price))
self.high_label.setText('High: ' + str(self.high_price))
self.low_label.setText('Low: ' + str(self.low_price))
def build_model_data(self, ticker, time_frame):
raw_data = load_rawdata(ticker, time_frame)
data_stats = stockstats.StockDataFrame.retype(raw_data.copy())
raw_data['trend'] = self.build_trend_data(raw_data)
if time_frame == 'monthly':
raw_data['ema'] = (data_stats["close_12_ema"].round(2))
else:
raw_data['ema'] = (data_stats["close_20_ema"].round(2))
raw_data['rsi'] = data_stats['rsi_6']
raw_data['cci'] = data_stats['cci']
raw_data['macd'] = data_stats['macd']
raw_data['kdjk'] = data_stats['kdjk']
raw_data['kdjd'] = data_stats['kdjd']
raw_data['wr'] = data_stats['wr_6']
columns = ['h-l', 'c-o', 'h-cp', 'l-cp', 'diff_ema', 'diff_volume', 'cci', 'rsi', 'macd', 'kdjk', 'kdjd', 'wr',
'trend']
new_columns = []
# create new columns name
for i in range(self.look_hist_window):
offset = i + 1
for indicator in columns:
new_columns.append(indicator + '_' + str(offset))
# create new data frame
model_data = pd.DataFrame(columns=new_columns)
# use look_hist_window(equals 4 default) candles to build the input data of current candles trend
raw_data['h-l'] = raw_data['high'] - raw_data['low']
raw_data['c-o'] = raw_data['open'] - raw_data['low']
raw_data['h-cp'] = raw_data['high'] - raw_data['close'].shift(1)
raw_data['l-cp'] = raw_data['low'] - raw_data['close'].shift(1)
raw_data['diff_ema'] = raw_data['close'] - raw_data['ema']
raw_data['diff_volume'] = raw_data['volume'].pct_change()
for i in range(1, self.look_hist_window + 1):
model_data[new_columns[(i - 1) * 13:i * 13]] = raw_data[['h-l', 'c-o', 'h-cp', 'l-cp',
'diff_ema', 'diff_volume', 'cci', 'rsi',
'macd', 'kdjk', 'kdjd', 'wr','trend']].shift(i).copy()
# self.model_data = self.model_data.set_index(self.raw_data.index)
model_data = round(model_data, 2)
model_data['target'] = raw_data['trend']
self.save_model_data(ticker, time_frame, model_data)
def load_data(self):
for tf in self.tfs:
index = -1
for etf in self.etfs:
index += 1
data = load_rawdata(etf, tf)
if data is None:
continue
data_stats = stockstats.StockDataFrame.retype(data.copy())
data[['ema12', 'ema20']] = data_stats[['close_12_ema', 'close_20_ema']]
# 数值保留两位小数
data[['open', 'high', 'low', 'close', 'ema12', 'ema20']] = data[
['open', 'high', 'low', 'close', 'ema12', 'ema20']].round(decimals=2)
# ama = calculate_ama(data, data_stats)[0][-1]
# 计算ama
ama, _, _ = calculate_ama(data, data_stats)
ama = ama[-1]
# 获取倒数第二行数据，是最新的
tmp = data.iloc[-1]
dict = {'ETF': etf,
'current': tmp['close'],
'pre close': data.iloc[-2]['close'],
'net change': round(tmp['close'] - data.iloc[-2]['close']),
'pct change': round((tmp['close'] / data.iloc[-2]['close'] -1) * 100, 2),
'open': tmp['open'],
'high': tmp['high'],
'low': tmp['low'],
'volume': int(tmp['volume']),
'ama': round(ama, 2),
'ema12': tmp['ema12'],
'ema20': tmp['ema20']}
df = pd.DataFrame(dict, index=[index])
if tf == 'weekly':
self.weekly_data = self.weekly_data.append(df, ignore_index=True)
if tf == 'monthly':
self.monthly_data = self.monthly_data.append(df, ignore_index=True)
def __init__(self):
super(PositionCalculator, self).__init__()
# 获取本地的etf名称列表
try:
self.etfs = np.load('./Data/etfs.npy').tolist()
except FileNotFoundError:
self.etfs = [
'SPY', 'QQQ', 'TLT', 'GLD', 'IWM', 'EFA', 'HYG', 'XLV'
]
# 创建atr列表
self.atr_list = []
# get ATR
self.lines = []
for i in range(len(self.etfs)):
raw_data = load_rawdata(self.etfs[i], 'daily')
if raw_data is None:
continue
data_stats = stockstats.StockDataFrame.retype(raw_data.copy())
atr = round(data_stats['atr'].iloc[-1], 2)
self.atr_list.append(atr)
#计算周上面的五条线指标
raw_data = load_rawdata(self.etfs[i], 'weekly')
data_stats = stockstats.StockDataFrame.retype(raw_data.copy())
BB = round(data_stats["close_20_ema"][-1], 2)
UBB = round(BB + 2 * data_stats["close_20_mstd"][-1], 2)
plus_half_sd = round(BB + 0.5 * data_stats["close_20_mstd"][-1], 2)
minus_half_sd = round(BB - 0.5 * data_stats["close_20_mstd"][-1],
2)
LBB = round(BB - 2 * data_stats["close_20_mstd"][-1], 2)
self.lines.append([UBB, plus_half_sd, BB, minus_half_sd, LBB])
# 开始创建页面
# 设置主要字体
self.myfont = QFont('Arial', 12, QFont.Bold)
# 页面的主要布局采用垂直布局
self.main_layout = QVBoxLayout(self)
# 创建顶部选择控件
top_widget = QWidget()
# 采用水平布局
top_layout = QHBoxLayout()
# 创建控件
capital_label = QLabel('Capital')
capital_label.setFont(self.myfont)
self.capital_lineedit = QLineEdit()
self.capital_lineedit.setPlaceholderText('ex: 100000')
self.capital_lineedit.setFont(self.myfont)
risk_label = QLabel('Risk tolerance')
risk_label.setFont(self.myfont)
self.risk_lineedit = QLineEdit()
self.risk_lineedit.setPlaceholderText('ex: 0.02')
self.risk_lineedit.setFont(self.myfont)
stop_coef_label = QLabel('Stop Coefficient')
stop_coef_label.setFont(self.myfont)
self.stop_coef_lineedit = QLineEdit()
self.stop_coef_lineedit.setPlaceholderText('ex: 0.1')
self.stop_coef_lineedit.setFont(self.myfont)
self.calculate_btn = QPushButton('Calculate')
self.calculate_btn.setFont(self.myfont)
self.calculate_btn.setShortcut(Qt.Key_Return)
self.calculate_btn.clicked.connect(self.show_position_table)
# 将这些控件加入top_layout
top_layout.addWidget(capital_label)
top_layout.addWidget(self.capital_lineedit)
top_layout.addWidget(risk_label)
top_layout.addWidget(self.risk_lineedit)
top_layout.addWidget(stop_coef_label)
top_layout.addWidget(self.stop_coef_lineedit)
top_layout.addWidget(self.calculate_btn)
# 设置top_widget的layout
top_widget.setLayout(top_layout)
top_widget.setContentsMargins(0, 40, 0, 40)
# 将top_widget加入到总的main_layout
self.main_layout.addWidget(top_widget)
# 创建显示position的表格
self.rows_name = [
'Current Price', 'Risk Exposure', 'Daily ATR', 'Trend',
'Trend Score', 'Position', 'Entry Proximal', 'Entry Distal',
'Target', 'Stop Price', 'Position Size', 'Position Value'
]
self.position_table = QTableWidget()
self.position_table.setColumnCount(len(self.etfs))
self.position_table.setRowCount(len(self.rows_name))
# 设置宽度自由扩展
self.position_table.horizontalHeader().setMinimumHeight(60)
self.position_table.horizontalHeader().setDefaultAlignment(
Qt.AlignCenter)
self.position_table.horizontalHeader().setSectionResizeMode(
QHeaderView.Stretch)
self.position_table.verticalHeader().setSectionResizeMode(
QHeaderView.Stretch)
self.position_table.verticalHeader().setDefaultAlignment(
Qt.AlignCenter)
# 设置表头高度
self.position_table.horizontalHeader().setMinimumHeight(60)
self.position_table.horizontalHeader().setFont(self.myfont)
self.position_table.verticalHeader().setFont(self.myfont)
# 设置表头内容
self.position_table.setHorizontalHeaderLabels(self.etfs)
self.position_table.setVerticalHeaderLabels(self.rows_name)
# 禁止编辑
self.position_table.setEditTriggers(QAbstractItemView.NoEditTriggers)
# 隐藏纵向header
#self.position_table.verticalHeader().setVisible(False)
# 将表格加入main_layout
self.main_layout.addWidget(self.position_table)
