def macd_side(close):
macd, signal, hist = talib.MACD(close.values)
hist = pd.Series(hist).fillna(1).values
return pd.Series(2 * ((hist > 0).astype(float) - 0.5),
index=close.index[-len(hist):])
def MACD(self,se,fast=6,slow=12,signal=9):
macd, signal, hist = talib.MACD(np.array(se),fastperiod=fast, slowperiod=slow, signalperiod=signal)
return macd,signal,hist
def CalculateIndicators(self):
#Get all available stocks
stocks =[x['BBGTicker'] for x in list(self.db.Stocks.find({}))]
#For each stocks get price and volume and calculate analytics
for s in stocks:
print(s)
#Get adjusted close price if available else close price
c = self.db.Prices.find_one({'BBGTicker' : s},{'Adj Close' : 1})
if 'Adj Close' not in list(c.keys()):
c = self.db.Prices.find_one({'BBGTicker' : s},{'Close' : 1})
close=c['Close']
else:
close=c['Adj Close']
#Get adjusted low price if available else low price
l = self.db.Prices.find_one({'BBGTicker' : s},{'Adj Low' : 1})
if 'Adj Low' not in list(l.keys()):
l = self.db.Prices.find_one({'BBGTicker' : s},{'Low' : 1})
low=l['Low']
else:
low=l['Adj Low']
#Get adjusted high price if available else high price
h = self.db.Prices.find_one({'BBGTicker' : s},{'Adj High' : 1})
if 'Adj High' not in list(h.keys()):
h = self.db.Prices.find_one({'BBGTicker' : s},{'High' : 1})
high=h['High']
else:
high=h['Adj High']
#Get adjusted volume if available else volume
v = self.db.Prices.find_one({'BBGTicker' : s},{'Adj Volume' : 1})
if 'Adj Volume' not in list(v.keys()):
v = self.db.Prices.find_one({'BBGTicker' : s},{'Volume' : 1})
vol=v['Volume']
else:
vol=v['Adj Volume']
dates = sorted(close.keys())
#Transform dictionary into numpy array
closeP = numpy.zeros(shape=(len(dates),))
highP = numpy.zeros(shape=(len(dates),))
lowP = numpy.zeros(shape=(len(dates),))
volume = numpy.zeros(shape=(len(dates),))
i=0
for dt in dates:
closeP[i] = close[dt]
highP[i] = high[dt]
lowP[i] = low[dt]
volume[i] = vol[dt]
i=i+1
#Calculate different moving average
rawsma = talib.SMA(closeP,timeperiod=14)
rawema = talib.EMA(closeP,timeperiod=30)
rawkama = talib.KAMA(closeP,timeperiod=30)
sma=numpy.copy(rawsma)
ema=numpy.copy(rawema)
kama=numpy.copy(rawkama)
#Transform moving average into signal (MA - close price)
for k in range(len(closeP)):
sma[k] = rawsma[k]-closeP[k]
ema[k] = rawema[k]-closeP[k]
kama[k] = rawkama[k]-closeP[k]
#Calculate different analytics (RSI, ADX, MFI, CCI OBV etc)
adx = talib.ADX(highP,lowP,closeP,timeperiod=14)
rsi = talib.RSI(closeP,timeperiod=14)
cci = talib.CCI(highP,lowP,closeP,timeperiod=14)
macd, signal, hist = talib.MACD(closeP,fastperiod=12,slowperiod=26,signalperiod=9)
mfi = talib.MFI(highP,lowP,closeP,volume,timeperiod=14)
ad = talib.AD(highP,lowP,closeP,volume)
adosc = talib.ADOSC(highP,lowP,closeP,volume,fastperiod=3, slowperiod=10)
atr = talib.ATR(highP,lowP,closeP,timeperiod=14)
obv = talib.OBV(closeP,volume)
slowk, slowd = talib.STOCH(highP,lowP,closeP,fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
mom = talib.MOM(closeP,timeperiod=10)
roc = talib.ROC(closeP,timeperiod=10)
upperBB, middleBB, lowerBB = talib.BBANDS(closeP, matype=MA_Type.T3)
ht = talib.HT_TRENDLINE(closeP)
willR = talib.WILLR(highP,lowP,closeP,timeperiod=14)
#Define dictionary keys
analytics = {'BBGTicker' : s}
analytics['RAWSMA']={}
analytics['RAWEMA']={}
analytics['RAWKAMA']={}
analytics['SMA']={}
analytics['EMA']={}
analytics['KAMA']={}
analytics['ADX']={}
analytics['RSI']={}
analytics['CCI']={}
analytics['MACD']={}
analytics['MACDSIGNAL']={}
analytics['MACDHIST']={}
analytics['MFI']={}
analytics['AD']={}
analytics['ADOSCILLATOR']={}
analytics['ATR']={}
analytics['OBV']={}
analytics['STOCHSLOWD']={}
analytics['STOCHSLOWK']={}
analytics['MOM']={}
analytics['ROC']={}
analytics['LOWERBB']={}
analytics['MIDDLEBB']={}
analytics['UPPERBB']={}
analytics['HILBERT']={}
analytics['WILLIAMR']={}
#For each date dt insert into corresponding dictionary
i=0
for dt in dates:
analytics['RAWSMA'][dt]=rawsma[i]
analytics['RAWEMA'][dt]=rawema[i]
analytics['RAWKAMA'][dt]=rawkama[i]
analytics['SMA'][dt]=sma[i]
analytics['EMA'][dt]=ema[i]
analytics['KAMA'][dt]=kama[i]
analytics['ADX'][dt]=adx[i]
analytics['RSI'][dt]=rsi[i]
analytics['CCI'][dt]=cci[i]
analytics['MACD'][dt]=macd[i]
analytics['MACDSIGNAL'][dt]=signal[i]
analytics['MACDHIST'][dt]=hist[i]
analytics['MFI'][dt]=mfi[i]
analytics['AD'][dt]=ad[i]
analytics['ADOSCILLATOR'][dt]=adosc[i]
analytics['ATR'][dt]=atr[i]
analytics['OBV'][dt]=obv[i]
analytics['STOCHSLOWD'][dt]=slowd[i]
analytics['STOCHSLOWK'][dt]=slowk[i]
analytics['MOM'][dt]=mom[i]
analytics['ROC'][dt]=roc[i]
analytics['LOWERBB'][dt]=lowerBB[i]
analytics['MIDDLEBB'][dt]=middleBB[i]
analytics['UPPERBB'][dt]=upperBB[i]
analytics['HILBERT'][dt]=ht[i]
analytics['WILLIAMR'][dt]=willR[i]
i=i+1
#Insert data into mongo DB
record = [analytics]
self.db.Analytics.delete_many({'BBGTicker' : s})
self.db.Analytics.insert_many(record)
#Calculate future returns for horizon between 1 and 30 days
futureReturn = {'BBGTicker' : s}
for k in range(60):
k=k+1
futureReturn['FUTURERETURN'+str(k)+'DAYS'] = {}
for dt in dates:
p1 = close[dt]
dateIndex = dates.index(dt)
if dateIndex + k < len(dates):
d2 = dates[dateIndex + k] #Lookup corresponding future date for horizon k
p2 = close[d2]
futureReturn['FUTURERETURN'+str(k)+'DAYS'][dt] = p2/p1-1 #Calculate future return
#Insert future returns into Mongo DB
record = [futureReturn]
self.db.Returns.delete_many({'BBGTicker' : s})
self.db.Returns.insert_many(record)
def tickerti (tickertxt):
ticker = tickertxt
today = date.today ().strftime ("%Y-%m-%d")
# print(today)
# print(ticker)
stock_data = yf.download(ticker, start="2020-04-01", end=today)
st.table(stock_data.tail())
plt.plot(stock_data['Close'], color='blue')
plt.title("Daily Close Price (ticker)")
st.pyplot (plt , use_container_width=True)
# plt.show()
stock_data['SMA_20'] = talib.SMA(stock_data['Close'], timeperiod=20)
st.table (stock_data['SMA_20'].tail())
stock_data['SMA_50'] = talib.SMA(stock_data['Close'], timeperiod=50)
st.table (stock_data['SMA_50'].tail())
plt.plot(stock_data['Close'], color='blue', label='Daily Close Price')
plt.plot(stock_data['SMA_20'], color='green', label='SMA 20')
plt.plot(stock_data['SMA_50'], color='red', label='SMA 50')
plt.legend()
plt.title('Simple Moving Averages')
st.pyplot (plt , use_container_width=True)
# plt.show()
stock_data['EMA_20'] = talib.EMA(stock_data['Close'], timeperiod=20)
st.table(stock_data['SMA_20'].tail())
stock_data['EMA_50'] = talib.EMA(stock_data['Close'], timeperiod=50)
st.table(stock_data['SMA_50'].tail())
plt.plot(stock_data['Close'], color='blue', label='Daily Close Price')
plt.plot(stock_data['EMA_20'], color='green', label='EMA 20')
plt.plot(stock_data['EMA_50'], color='red', label='EMA 50')
plt.legend()
plt.title('Exponential Moving Averages')
st.pyplot (plt , use_container_width=True)
# plt.show()
plt.plot(stock_data['Close'], color='blue', label='Daily Close Price')
plt.plot(stock_data['SMA_50'], color='green', label='SMA 50')
plt.plot(stock_data['EMA_50'], color='red', label='EMA 50')
plt.legend()
plt.title('SMA 50 vs EMA 50')
st.pyplot (plt , use_container_width=True)
# plt.show()
stock_data['ADX'] = talib.ADX(stock_data['High'], stock_data['Low'], stock_data['Close'], timeperiod=14)
st.table(stock_data['ADX'].tail())
fig, (ax1, ax2) = plt.subplots(2, sharex=True)
ax1.set_ylabel('Price')
ax1.plot(stock_data['Close'])
ax2.set_ylabel('ADX')
ax2.plot(stock_data['ADX'], color='green')
ax1.set_title('Daily Close Price and ADX')
ax2.axhline(y = 50, color = 'r', linestyle = '-')
ax2.axhline(y = 25, color = 'r', linestyle = '-')
st.pyplot (plt , use_container_width=True)
# plt.show()
macd, macdsignal, macdhist = talib.MACD(stock_data['Close'], fastperiod=12, slowperiod=26, signalperiod=9)
fig, (ax1, ax2) = plt.subplots(2, sharex=True)
ax1.set_ylabel('Price')
ax1.plot(stock_data['Close'])
ax2.set_ylabel('MACD')
ax2.plot(macdsignal, color='green', label='Signal Line')
ax2.plot(macd, color='red', label='MACD')
ax2.bar(macdhist.index, macdhist, color='purple')
ax1.set_title('Daily Close Price and MACD')
plt.legend()
st.pyplot (plt , use_container_width=True)
# plt.show()
stock_data['RSI'] = talib.RSI(stock_data['Close'], timeperiod=14)
st.table(stock_data['RSI'].tail())
fig, (ax1, ax2) = plt.subplots(2, sharex=True)
ax1.set_ylabel('Price')
ax1.plot(stock_data['Close'])
ax2.set_ylabel('RSI')
ax2.plot(stock_data['RSI'], color='green')
ax2.axhline(y = 70, color = 'r', linestyle = '-')
ax2.axhline(y = 30, color = 'r', linestyle = '-')
ax1.set_title('Daily Close Price and RSI')
st.pyplot (plt , use_container_width=True)
# plt.show()
upper, mid, lower = talib.BBANDS(stock_data['Close'], nbdevup=2, nbdevdn=2, timeperiod=20)
plt.plot(upper, label="Upper band")
plt.plot(mid, label='Middle band')
plt.plot(lower, label='Lower band')
plt.title('Bollinger Bands')
plt.legend()
st.pyplot (plt , use_container_width=True)
def generate_tech_data(p1_df,p2_df):
sample = pd.DataFrame({'p1': p1_df.values.ravel(), 'p2': p2_df.values.ravel()}, index=p1_df.index)
p1=p1_df.values.ravel()
p2=p2_df.values.ravel()
sample['p1'+'_mom'] = talib.MOM(p1)
sample['p1' + '_macd'], sample['p1' + '_macd_sig'], sample['p1' + '_macd_hist'] = talib.MACD(p1)
sample['p1' + '_rsi'] = talib.RSI(p1, timeperiod=10)
sample['p1' + '_cmo'] = talib.CMO(p1)
sample['p2' + '_mom'] = talib.MOM(p2)
sample['p2' + '_macd'], sample['p2' + '_macd_sig'], sample['p2' + '_macd_hist'] = talib.MACD(p2)
sample['p2' + '_rsi'] = talib.RSI(p2, timeperiod=10)
sample['p2' + '_cmo'] = talib.CMO(p2)
spread = p1 / p2
sample['spread'] = spread
sample['diff'] = sample['spread'] / sample['spread'].shift(1)
sample = sample.dropna()
return sample
def handle_bar(context, bar_dict):
end_date = bar_dict[context.s1].datetime.strftime('%Y-%m-%d')
start_date = (datetime.strptime(end_date, '%Y-%m-%d') - timedelta(
days=400)).strftime('%Y-%m-%d')
print("start_date: %s " % start_date)
print("end_date: %s " % end_date)
stock_history = context.stock_price
# # check suspend
# if stock_history.loc[(stock_history['date'] == end_date)].empty:
# print("suspend happens on %s " % end_date)
# return
# stock_history = pd.read_csv('/Users/keli/Documents/Quant/Stock_data/price_pre', sep='|')
prices = stock_history.loc[(stock_history['date'] >= start_date) & (stock_history['date'] <= end_date) & (
stock_history['stock_code'] == context.s1)]['close'].values
prices = np.array(prices)[len(prices) - 200:]
macd, signal, hist = talib.MACD(prices, 12, 26, 9)
# print ("history hist: ",2 * hist[-1])
if context.first_time:
context.last = hist[-1]
context.first_time = False
else:
if hist[-1] < 0 and context.last > 0:
print ("@@@@@@@@@@ sell signal - hist is moving up->down across the axis @@@@@@@@@@")
# print ("current hist: %s " % (str(2 * hist[-1])))
# print ("previous hist: %s " % (str(2 * context.last)))
print(end_date)
order_percent(context.s1, -1)
elif hist[-1] > 0 and context.last < 0:
print ("@@@@@@@@@@ buy signal - hist is moving down->up across the axis @@@@@@@@@@")
# print ("current hist: %s " % (str(2 * hist[-1])))
# print ("previous hist: %s " % (str(2 * context.last)))
# print(end_date)
# order_percent(context.s1, 1)
macd_60m(context, bar_dict)
if hist[-1] > 0 and context.last > 0 and abs(hist[-1]) < abs(context.last):
print (
"@@@@@@@@@@ sell signal - hist is above the axis and current hist is less than previous hist @@@@@@@@@@")
# print ("current hist: %s " % (str(2 * hist[-1])))
# print ("previous hist: %s " % (str(2 * context.last)))
# print("prices: ",prices)
print (end_date)
# macd_60m(context,bar_dict)
order_percent(context.s1, -1)
elif hist[-1] < 0 and context.last < 0 and abs(hist[-1]) < abs(context.last):
print (
"@@@@@@@@@@ buy signal - hist is below the axis and current hist is larger than previous hist @@@@@@@@@@")
# print ("current hist: %s " % (str(2 * hist[-1])))
# print ("previous hist: %s " % (str(2 * context.last)))
print(end_date)
macd_60m(context, bar_dict)
# if context.portfolio.cash > prices[-1] * 100:
# print(context.portfolio)
# print("place order")
# order_value(context.s1, context.portfolio.cash)
# else:
# print("skip order, no money")
context.last = hist[-1]
def add_macd_indicators(self, df):
''' minimum data size: 11198 '''
# factor_9t = self.daily_count * 25
factor_8t = self.daily_count * 20
factor_7t = self.daily_count * 15
factor_6t = self.daily_count * 10
factor_5t = self.daily_count * 5
factor_4t = self.daily_count * 4
factor_3t = self.daily_count * 3
factor_2t = self.daily_count * 2
factor_1t = self.daily_count * 1
factor_1h = 4
# df['macd_9t'], df['macdsignal_9t'], df['machhist_9t'] = talib.MACD(df.close, fastperiod=12*factor_9t, slowperiod=26*factor_9t, signalperiod=9*factor_9t)
df['macd_8t'], df['macdsignal_8t'], df['machhist_8t'] = talib.MACD(
df.close,
fastperiod=12 * factor_8t,
slowperiod=26 * factor_8t,
signalperiod=9 * factor_8t)
df['macd_7t'], df['macdsignal_7t'], df['machhist_7t'] = talib.MACD(
df.close,
fastperiod=12 * factor_7t,
slowperiod=26 * factor_7t,
signalperiod=9 * factor_7t)
df['macd_6t'], df['macdsignal_6t'], df['machhist_6t'] = talib.MACD(
df.close,
fastperiod=12 * factor_6t,
slowperiod=26 * factor_6t,
signalperiod=9 * factor_6t)
df['macd_5t'], df['macdsignal_5t'], df['machhist_5t'] = talib.MACD(
df.close,
fastperiod=12 * factor_5t,
slowperiod=26 * factor_5t,
signalperiod=9 * factor_5t)
df['macd_4t'], df['macdsignal_4t'], df['machhist_4t'] = talib.MACD(
df.close,
fastperiod=12 * factor_4t,
slowperiod=26 * factor_4t,
signalperiod=9 * factor_4t)
df['macd_3t'], df['macdsignal_3t'], df['machhist_3t'] = talib.MACD(
df.close,
fastperiod=12 * factor_3t,
slowperiod=26 * factor_3t,
signalperiod=9 * factor_3t)
df['macd_2t'], df['macdsignal_2t'], df['machhist_2t'] = talib.MACD(
df.close,
fastperiod=12 * factor_2t,
slowperiod=26 * factor_2t,
signalperiod=9 * factor_2t)
df['macd_1t'], df['macdsignal_1t'], df['machhist_1t'] = talib.MACD(
df.close,
fastperiod=12 * factor_1t,
slowperiod=26 * factor_1t,
signalperiod=9 * factor_1t)
df['macd_1h'], df['macdsignal_1h'], df['machhist_1h'] = talib.MACD(
df.close,
fastperiod=12 * factor_1h,
slowperiod=26 * factor_1h,
signalperiod=9 * factor_1h)
return df
def _update_ta(self):
"""更新辅助技术指标"""
if not self.ma:
ma_temp = dict()
close_ = np.array([x["close"] for x in self.kline_raw],
dtype=np.double)
for p in self.ma_params:
ma_temp['ma%i' % p] = ta.SMA(close_, p)
for i in range(len(self.kline_raw)):
ma_ = {
'ma%i' % p: ma_temp['ma%i' % p][i]
for p in self.ma_params
}
ma_.update({"dt": self.kline_raw[i]['dt']})
self.ma.append(ma_)
else:
ma_ = {
'ma%i' % p: sum([x['close'] for x in self.kline_raw[-p:]]) / p
for p in self.ma_params
}
ma_.update({"dt": self.kline_raw[-1]['dt']})
if self.verbose:
print("ma new: %s" % str(ma_))
if self.kline_raw[-2]['dt'] == self.ma[-1]['dt']:
self.ma.append(ma_)
else:
self.ma[-1] = ma_
assert self.ma[-2]['dt'] == self.kline_raw[-2]['dt']
if not self.macd:
close_ = np.array([x["close"] for x in self.kline_raw],
dtype=np.double)
# m1 is diff; m2 is dea; m3 is macd
m1, m2, m3 = ta.MACD(close_,
fastperiod=12,
slowperiod=26,
signalperiod=9)
for i in range(len(self.kline_raw)):
self.macd.append({
"dt": self.kline_raw[i]['dt'],
"diff": m1[i],
"dea": m2[i],
"macd": m3[i]
})
else:
close_ = np.array([x["close"] for x in self.kline_raw[-200:]],
dtype=np.double)
# m1 is diff; m2 is dea; m3 is macd
m1, m2, m3 = ta.MACD(close_,
fastperiod=12,
slowperiod=26,
signalperiod=9)
macd_ = {
"dt": self.kline_raw[-1]['dt'],
"diff": m1[-1],
"dea": m2[-1],
"macd": m3[-1]
}
if self.verbose:
print("macd new: %s" % str(macd_))
if self.kline_raw[-2]['dt'] == self.macd[-1]['dt']:
self.macd.append(macd_)
else:
self.macd[-1] = macd_
assert self.macd[-2]['dt'] == self.kline_raw[-2]['dt']
def to_df(self,
ma_params=(5, 20),
use_macd=False,
max_count=1000,
mode="raw"):
"""整理成 df 输出
:param ma_params: tuple of int
均线系统参数
:param use_macd: bool
:param max_count: int
:param mode: str
使用K线类型， raw = 原始K线，new = 去除包含关系的K线
:return: pd.DataFrame
"""
if mode == "raw":
bars = self.kline_raw[-max_count:]
elif mode == "new":
bars = self.kline_raw[-max_count:]
else:
raise ValueError
fx_list = {
x["dt"]: {
"fx_mark": x["fx_mark"],
"fx": x['fx']
}
for x in self.fx_list[-(max_count // 2):]
}
bi_list = {
x["dt"]: {
"fx_mark": x["fx_mark"],
"bi": x['bi']
}
for x in self.bi_list[-(max_count // 4):]
}
xd_list = {
x["dt"]: {
"fx_mark": x["fx_mark"],
"xd": x['xd']
}
for x in self.xd_list[-(max_count // 8):]
}
results = []
for k in bars:
k['fx_mark'], k['fx'], k['bi'], k['xd'] = "o", None, None, None
fx_ = fx_list.get(k['dt'], None)
bi_ = bi_list.get(k['dt'], None)
xd_ = xd_list.get(k['dt'], None)
if fx_:
k['fx_mark'] = fx_["fx_mark"]
k['fx'] = fx_["fx"]
if bi_:
k['bi'] = bi_["bi"]
if xd_:
k['xd'] = xd_["xd"]
results.append(k)
df = pd.DataFrame(results)
for p in ma_params:
df.loc[:, "ma{}".format(p)] = ta.SMA(df.close.values, p)
if use_macd:
diff, dea, macd = ta.MACD(df.close.values)
df.loc[:, "diff"] = diff
df.loc[:, "dea"] = diff
df.loc[:, "macd"] = diff
return df
path2 = os.path.join(parent_folder, 'simulator')
sys.path.append(path2)
from basic_ploter import BasicPloter
from basic_simulator import BasicSimulator
# -------------------------------------------------------------
# -------------------------------------------------------------
# data
# -------------------------------------------------------------
instrument = 'GBP_USD'
data_file = '{}.csv'.format(instrument)
data_folder = os.path.join(parent_folder, 'data')
date_file_path = os.path.join(data_folder, data_file)
# -------------------------------------------------------------
df = pd.read_csv(date_file_path)
closeMidData = df['closeMid'].values
dateData = df['date'].values
macd, macdsignal, macdhist = talib.MACD(closeMidData,
fastperiod=12,
slowperiod=26,
signalperiod=9)
forex_ploter = BasicPloter()
forex_ploter.plot_basic_trend(dateData, closeMidData, title=instrument)
forex_ploter.plot_MACD(dateData,
closeMidData,
macd,
macdsignal,
macdhist,
title=instrument)
def plot(self, symbolId):
start = datetime.datetime(2020, 1, 1)
end = datetime.datetime.today()
for stock in symbolId:
stock = str(stock)
df = web.DataReader([stock + '.TW'], 'yahoo', start, end)
ma5 = talib.SMA(df['Close'][stock + '.TW'], timeperiod=5)
ma20 = talib.SMA(df['Close'][stock + '.TW'], timeperiod=20)
ma10 = talib.SMA(df['Close'][stock + '.TW'], timeperiod=10)
dif, dem, hist = talib.MACD(df['Close'][stock + '.TW'])
ema12 = talib.EMA(df['Close'][stock + '.TW'], 12)
ema26 = talib.EMA(df['Close'][stock + '.TW'], 26)
rsi5 = talib.RSI(df['Close'][stock + '.TW'].values, 5)
rsi10 = talib.RSI(df['Close'][stock + '.TW'].values, 10)
k, d, j = self.kd(df, stock)
df.index = df.index.to_series().apply(
lambda x: x.strftime('%Y-%m-%d'))
#畫圖
fig = plt.figure(figsize=(24, 16))
ax1 = fig.add_axes([0.05, 0.55, 0.9, 0.4])
plt.title(stock, fontsize=25, fontweight='bold')
ax2 = fig.add_axes([0.05, 0.4, 0.9, 0.15])
ax3 = fig.add_axes([0.05, 0.25, 0.9, 0.15])
ax4 = fig.add_axes([0.05, 0.1, 0.9, 0.15])
mpf.candlestick2_ochl(ax1,
df['Open'][stock + '.TW'],
df['Close'][stock + '.TW'],
df['High'][stock + '.TW'],
df['Low'][stock + '.TW'],
width=0.6,
colorup='r',
colordown='g',
alpha=0.75)
ax1.plot(ma5.values, label='MA5')
ax1.plot(ma10.values, label='MA10')
ax1.plot(ma20.values, label='MA20')
ax1.legend()
ax2.plot(dif, label='DIF')
ax2.plot(dem, label='DEM')
ax2.fill_between(hist.index, 0, hist, label='HIST')
ax2.legend()
ax3.plot(k.values, label='K')
ax3.plot(d.values, label='D')
ax3.plot(j.values, '--', label='J')
ax3.legend()
ax4.plot(rsi5, label='RSI5')
ax4.plot(rsi10, label='RSI10')
ax4.legend()
ax4.set_xticks(range(0, len(df.index), 10))
ax4.set_xticklabels(df.index[::10])
plt.savefig('技術分析圖' + stock + '.png')
plt.show()
rsi_entry_length = 405
rsi_exit_length = 362
rsi_entry_filter = 50
rsi_exit_filter = 52
fastLength = 30
slowLength = 57
signalLength = 4
exit_fastLength = 30
exit_slowLength = 34
exit_signalLength = 4
Macd, Macdsignal, Macdhist = ta.MACD(close,
fastperiod=fastLength,
slowperiod=slowLength,
signalperiod=signalLength)
exit_Macd, exit_Macdsignal, exit_Macdhist = ta.MACD(
close,
fastperiod=exit_fastLength,
slowperiod=exit_slowLength,
signalperiod=exit_signalLength)
Week_Macd, Week_Macdsignal, Week_Macdhist = ta.MACD(
weekclose,
fastperiod=Week_fastLength,
slowperiod=Week_slowLength,
signalperiod=Week_signalLength)
entry_Rsi = ta.RSI(close, rsi_entry_length)
exit_Rsi = ta.RSI(close, rsi_exit_length)
t_high = np.transpose(x[:,[1]])
t_low = np.transpose(x[:,[2]])
t_vol = np.transpose(x[:,[3]])
t_y = np.transpose(x[:,[4]])
#array로 변환
open = np.array(t_open[0],dtype='float')
high = np.array(t_high[0],dtype='float')
low = np.array(t_low[0],dtype='float')
volume = np.array(t_vol[0],dtype='float')
close = np.array(t_y[0],dtype='float')
#------------------------------------------------------------------------------
#주가데이터를 지표데이터로 변환(TA-Lib 이용)
ma = ta.MA(close,timeperiod=5) #moving average
dmi = ta.DX(high,low,close,timeperiod=5)#direct movement index
macd, macdsignal, macdhist = ta.MACD(close,fastperiod=6, slowperiod=12,signalperiod=4) #moving average convergence/divergence
#------------------------------------------------------------------------------
#x를 지표데이터 15개 묶음으로 변경
x = np.c_[ma,dmi,macd, macdsignal, macdhist]
#***************************************************************************************************#여기서부터 사용하는 변수 다름
# train Parameters
R_seq_length = 10 #10일 데이치를 넣어
R_after = 9 #그로부터 10일뒤 종가 예측
R_data_dim = 5 #지표로 변환하여 데이터 5개가 들어감
R_hidden_dim = 10 #lstm에서 output은 10개가 나옴
R_output_dim = 1 #FC를 통해 10개에서 1개로
R_learning_rate = 0.01 #학습률
R_iterations = 1000 #반복 횟수
#X,Y
df_amd_bb_upper, df_amd_bb_middle, df_amd_bb_lower = ta.BBANDS(df_amd['Close'], timeperiod=timeperiod)
# Directionalmovementindex
df_amd_dx = ta.DX(df_amd['Close'],df_amd['Open'],df_amd['High'], timeperiod=timeperiod)
# Exponentialmovingaverages
df_amd_ema = ta.EMA(df_amd['Close'], timeperiod=timeperiod)
# Stochasticindex
df_amd_slowk, df_amd_slowd = ta.STOCH(df_amd['High'], df_amd['Low'], df_amd['Close'], fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
# Movingaverages
df_amd_ma5 = ta.MA(df_amd['Close'], timeperiod=timeperiod)
# MACD
df_amd_macd, df_amd_macdsignal, df_amd_macdhist = ta.MACD(df_amd['Close'], fastperiod=12, slowperiod=26, signalperiod=9)
# Relativestrengthindex
df_amd_rsi = ta.RSI(df_amd['Close'], timeperiod=timeperiod)
df_amd_total = df_amd.copy()
df_amd_total = pd.concat([df_amd_total,
df_amd_bb_upper, df_amd_bb_middle, df_amd_bb_lower, # Bollingerbands
df_amd_dx, # Directionalmovementindex
df_amd_ema, # Exponentialmovingaverages
df_amd_slowk, df_amd_slowd, # Stochasticindex
df_amd_ma5, # Movingaverages
df_amd_macd, df_amd_macdsignal, df_amd_macdhist, # MACD
df_amd_rsi], axis = 1) # Relativestrengthindex
def Get_MACD(df, dtype='d', days=ct.Power_Ma_Days, lastday=ct.Power_last_da):
# 参数12,26,9
df = df.sort_index(ascending=True)
if len(df) > 1 + lastday:
if lastday != 0:
df = df[:-lastday]
if len(df) < limitCount:
return (df, 1)
# df=df.fillna(0)
macd, macdsignal, macdhist = ta.MACD(np.array(df['close']),
fastperiod=5,
slowperiod=34,
signalperiod=5)
SignalMA5 = ta.MA(macdsignal, timeperiod=5, matype=0)
SignalMA10 = ta.MA(macdsignal, timeperiod=10, matype=0)
SignalMA20 = ta.MA(macdsignal, timeperiod=20, matype=0)
# 13-15 DIFF DEA DIFF-DEA
df['diff%s' % dtype] = pd.Series(macd, index=df.index) # DIFF 13
df['dea%s' % dtype] = pd.Series(macdsignal, index=df.index) # DEA 14
df['ddea%s' % dtype] = pd.Series(macdhist, index=df.index) # DIFF-DEA 15
dflen = df.shape[0]
MAlen = len(SignalMA5)
operate = 0
dd = df.dropna()
print dd[['diffd', 'dead', 'ddead']], dd.ddead.argmin()
import ipdb
ipdb.set_trace()
# 2个数组 1.DIFF、DEA均为正，DIFF向上突破DEA，买入信号。 2.DIFF、DEA均为负，DIFF向下跌破DEA，卖出信号。
# 待修改
diff = df.loc[df.index[-1], 'diff%s' % dtype]
diff2 = df.loc[df.index[-2], 'diff%s' % dtype]
dea = df.loc[df.index[-1], 'dea%s' % dtype]
dea2 = df.loc[df.index[-2], 'dea%s' % dtype]
if diff > 0:
if dea > 0:
if diff > dea and diff2 <= dea2:
operate = operate + 10 # 买入
else:
if dea < 0:
if diff == dea2:
operate = operate - 10 # 卖出
# 3.DEA线与K线发生背离，行情反转信号。
ma5 = df.loc[df.index[-1], 'ma5%s' % dtype] # 7
ma10 = df.loc[df.index[-1], 'ma10%s' % dtype] # 8
ma20 = df.loc[df.index[-1], 'ma20%s' % dtype] # 9
if ma5 >= ma10 and ma10 >= ma20: # K线上涨
if SignalMA5[MAlen - 1] <= SignalMA10[MAlen - 1] and SignalMA10[
MAlen - 1] <= SignalMA20[MAlen - 1]: # DEA下降
operate = operate - 1
elif ma5 <= ma10 and ma10 <= ma20: # K线下降
if SignalMA5[MAlen - 1] >= SignalMA10[MAlen - 1] and SignalMA10[
MAlen - 1] >= SignalMA20[MAlen - 1]: # DEA上涨
operate = operate + 1
# 4.分析MACD柱状线，由负变正，买入信号。
diffdea = df.loc[df.index[-1], 'ddea%s' % dtype]
# diffdea2 = df.loc[df.index[-2],'macdhist%s'%dtype]
if diffdea > 0 and dflen > 30:
for i in range(1, 26):
if df.loc[df.index[-1 - i], 'ddea%s' % dtype] <= 0:
operate = operate + 5
break
# 由正变负，卖出信号
if diffdea < 0 and dflen > 30:
dayl = 26 if len(df) > 26 else len(df)
for i in range(1, ):
if df.loc[df.index[-1 - i], 'ddea%s' % dtype] >= 0:
operate = operate - 5
break
for cl in ['diff%s' % dtype, 'dea%s' % dtype]:
operate = algoMultiTech(df, column=cl, days=days, op=operate)
df = df.sort_index(ascending=False)
return (df, operate)
data_stats = pd.DataFrame(index=stock_tickers, columns=header)
meanAcc = [0, 0, 0, 0]
meanAccStd = [0, 0, 0, 0]
meanFscore = [0, 0, 0, 0]
meanFscoreStd = [0, 0, 0, 0]
latex_confusion_matrices = ''
for ticker in stock_tickers:
hist = data[ticker].dropna()
rsi = pd.DataFrame(talib.RSI(hist['Close'])).fillna(0)
stoch = pd.DataFrame(
talib.STOCH(hist['High'], hist['Low'], hist['Close'])[0]).fillna(0)
macd = pd.DataFrame(talib.MACD(hist['Close'])[0]).fillna(0)
sma = pd.DataFrame(talib.SMA(hist['Close'], 10)).fillna(0)
will = pd.DataFrame(talib.WILLR(hist['High'], hist['Low'],
hist['Close'])).fillna(0)
mom = pd.DataFrame(talib.MOM(hist['Close'])).fillna(0)
# Number of days used for calculating indicators
init_days = 50
X = np.zeros((hist.shape[0], 6))
X[:,
0] = np.where(rsi > 70, -1,
np.where(rsi < 30, 1, np.where(rsi > rsi.shift(1), 1,
-1))).squeeze()
X[:, 1] = np.where(stoch > stoch.shift(1), 1, -1).squeeze()
X[:, 2] = np.where(macd > macd.shift(1), 1, -1).squeeze()
def macd_60m(context,bar_dict):
price_60 = pd.read_csv('/Users/keli/Documents/Quant/Stock_data/price_pre_600888_60m2', sep='|')
cur_date = (bar_dict[context.s1].datetime.strftime('%Y-%m-%d'))
previous_date = calTime(cur_date,-1)
last_date = calTime(cur_date,+1)
first_date = calTime(previous_date,-100)
previous_price_history = price_60.loc[
(price_60['date'] >= first_date) & (price_60['date'] < cur_date) & (
price_60['stock_code'] == context.s1)]['close'].values # dataframe
previous_price_history = np.array(previous_price_history)[len(previous_price_history) - 200:]
hist_60 = talib.MACD(previous_price_history, 12, 26, 9)[2]
last_hist = hist_60[-1]
rops = price_60.loc[
(price_60['date'] >= cur_date) & (price_60['date'] < last_date) & (
price_60['stock_code'] == context.s1)]['date'].values
# print ("rops: ", rops)
for i in range(len(rops)):
print ("current rop: ", rops[i])
previous_price_history = price_60.loc[
(price_60['date'] >= first_date) & (price_60['date'] <= rops[i]) & (
price_60['stock_code'] == context.s1)]['close'].values
previous_price_history = np.array(previous_price_history)[len(previous_price_history) - 200:]
hist_60 = talib.MACD(previous_price_history, 12, 26, 9)[2]
if hist_60[-1] < 0 and last_hist > 0:
print("@@@@@@@@@@ 60m sell signal - hist is moving up->down across the axis @@@@@@@@@@")
# print ("current hist: %s " % (str(2 * hist[-1])))
# print ("previous hist: %s " % (str(2 * context.last)))
order_percent(context.s1, -1)
# last_hist = hist_60[-1]
elif hist_60[-1] > 0 and last_hist < 0:
print("@@@@@@@@@@ 60m buy signal - hist is moving down->up across the axis @@@@@@@@@@")
# print ("current hist: %s " % (str(2 * hist[-1])))
# print ("previous hist: %s " % (str(2 * context.last)))
# print(end_date)
if context.portfolio.cash > previous_price_history[-1] * 100:
print("place order")
# order_value(context.s1, context.portfolio.cash)
order_percent(context.s1, 1)
print(context.portfolio)
else:
print("skip order, no money")
# last_hist = hist_60[-1]
elif hist_60[-1] > 0 and last_hist > 0 and abs(hist_60[-1]) < abs(last_hist):
print(
"@@@@@@@@@@ 60m sell signal - hist is above the axis and current hist is less than previous hist @@@@@@@@@@")
# print("current hist: %s " % (str(2 * hist_60[-1])))
# print("previous hist: %s " % (str(2 * last_hist)))
# print("prices: ", previous_price_history)
print("sell")
order_percent(context.s1, -1)
# last_hist = hist_60[-1]
elif hist_60[-1] < 0 and last_hist < 0 and abs(hist_60[-1]) < abs(last_hist):
print(
"@@@@@@@@@@ 60m buy signal - hist is below the axis and current hist is larger than previous hist @@@@@@@@@@")
# print("current hist: %s " % (str(2 * hist_60[-1])))
# print("previous hist: %s " % (str(2 * last_hist)))
if context.portfolio.cash > previous_price_history[-1] * 100:
print("place order")
# order_value(context.s1, context.portfolio.cash)
order_percent(context.s1, 1)
print(context.portfolio)
else:
print("skip order, no money")
last_hist = hist_60[-1]
def before_close(self):
if (self.pos_amount - len(self.positions)) > 0:
history = bao_stock.get_last_data(days_ago=40, fuquan=2)
history['code'] = history['code'].apply(lambda x: x.replace('.', ''))
codes = list(set(history['code'].tolist()))
dd = []
for i in range(0, len(codes), 500):
# 新浪获取实时数据不能一次获取全部，这里每次获取500只股票的数据
dd = dd + get_sina_live_data(codes[i:i+500])
current = pd.DataFrame(dd)
# 选出有用的列
current = current[['date', 'code', 'open', 'high', 'low', 'current', 'volume', 'amount', 'preclose']]
current.rename(columns={'current': 'close'}, inplace=True)
print(type(current.at[0, 'open']))
# 添加到历史数据中
history = history.append(current, ignore_index=True, sort=False)
history.sort_values(by=['code', 'date'], inplace=True)
# history['turn'] = history['turn'].apply(lambda x : x if x is np.nan else history['turn'].shift(1) * (history['volume'] / history['volume'].shift(1)))
# history['turn'] = history['turn'] if history['turn'] is np.nan else history['turn'].shift(1) * (history['volume'] / history['volume'].shift(1))
history['turn'].fillna(value=history['turn'].shift(1) * history['volume'] / history['volume'].shift(1), inplace=True)
history['pctChg'].fillna(value=(history['close'] - history['preclose']) / history['preclose'], inplace=True)
history.to_csv(config.OUTPUT_PATH + '/8888.csv', encoding='utf-8-sig', index=None)
print(history)
df = history
# 将时间字符串转换成datetime格式
df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d')
# 计算kdj
kdj(df)
# 计算rsi
rsi(df)
# 动量
df['mom'] = talib.MOM(df['close'], timeperiod=20)
# 波动率
df['atr'] = talib.ATR(df.high, df.low, df.close, timeperiod=20)
# macd
df['dif'], df['dea'], df['macd'] = talib.MACD(df['close'].values, fastperiod=12, slowperiod=26, signalperiod=9)
# SAR抛物线
df['sar'] = talib.SAR(df['high'], df['low'], acceleration=0.02, maximum=0.2)
# ADX
df['adx'] = talib.ADX(df.high, df.low, df.close, timeperiod=20)
# 多均线
df['ma_5'] = df['close'].rolling(5, min_periods=1).mean()
df['ma_10'] = df['close'].rolling(10, min_periods=1).mean()
df['ma_15'] = df['close'].rolling(15, min_periods=1).mean()
df['ma_50'] = df['close'].rolling(50, min_periods=1).mean()
df['ma_100'] = df['close'].rolling(100, min_periods=1).mean()
# macd买入信号
condition1 = (df['dif'] > df['dea'])
condition2 = (df['dif'].shift(1) < df['dea'].shift(1))
df.loc[condition1 & condition2, 'macd_b'] = 1
# macd卖出信号
condition3 = (df['dif'] < df['dea'])
condition4 = (df['dif'].shift(1) > df['dea'].shift(1))
df.loc[condition3 & condition4, 'macd_s'] = 1
# kdj买入信号
condition5 = (df['k'] > df['d'])
condition6 = (df['k'].shift(1) < df['d'].shift(1))
df.loc[condition5 & condition6, 'kdj_b'] = 1
# kdj卖出信号
condition7 = (df['k'] < df['d'])
condition8 = (df['k'].shift(1) > df['d'].shift(1))
df.loc[condition7 & condition8, 'kdj_s'] = 1
# rsi买入信号
condition5 = (df['rsi_6'] > df['rsi_24'])
condition6 = (df['rsi_6'].shift(1) < df['rsi_24'].shift(1))
df.loc[condition5 & condition6, 'rsi_b'] = 1
# rsi卖出信号
condition7 = (df['rsi_6'] < df['rsi_24'])
condition8 = (df['rsi_6'].shift(1) > df['rsi_24'].shift(1))
df.loc[condition7 & condition8, 'rsi_s'] = 1
# sar买入信号
condition9 = (df['close'] > df['sar'])
condition10 = (df['close'].shift(1) < df['sar'].shift(1))
df.loc[condition9 & condition10, 'sar_b'] = 1
# sar卖出信号
condition11 = (df['close'] < df['sar'])
condition12 = (df['close'].shift(1) > df['sar'].shift(1))
df.loc[condition11 & condition12, 'sar_s'] = 1
# 金叉多均线
condition13 = (df['close'] > df['ma_5'])
condition14 = (df['close'] > df['ma_10'])
condition15 = (df['close'] > df['ma_15'])
condition16 = (df['close'].shift(1) < df['ma_5'].shift(1))
condition17 = (df['close'].shift(1) < df['ma_10'].shift(1))
condition18 = (df['close'].shift(1) < df['ma_15'].shift(1))
df.loc[condition13 & condition14 & condition15 & condition16 & condition17 & condition18, 'ma3_b'] = 1
""" 开始筛选 """
today_df = df[df['date'] == self.today] # 今天的
today_df = today_df[today_df['kdj_b'] == 1] # 有信号的
today_df = today_df[today_df['close'] < 20] # 价格小于20 的
today_df = today_df[today_df['pctChg'] < 9] # 涨幅不超过9
today_df.sort_values(by=['turn'], ascending=False, inplace=True) # 按换手率排序
# 可用仓位
empty_pos = self.pos_amount - len(self.positions)
# 获取余额
accounts = self.sub_accounts.iloc[0]['可用金额']
# 获取可能需要的手续费
commission = accounts * self.commission
# 每个仓位的钱
one_money = (accounts - commission) / empty_pos
print(self.sub_accounts)
print(self.sub_accounts)
today_df = today_df[:empty_pos]
for c in today_df.iterrows():
now_df = get_sina_live_data([c['code'].replace('.', '')])
if now_df is None:
continue
now_df = now_df[0]
buy_money = round(now_df['current'] * (1 + self.slippage), 2)
buy_num = one_money / buy_money
buy_num = int(buy_num / 100) * 100
self.buy(code=c['code'].split('.')[1], price=buy_money, amount=buy_num)
self.get_positions()
def macd(self, fastPeriod, slowPeriod, signalPeriod, array=True):
macd, signal, hist = talib.MACD(self.close, fastPeriod, slowPeriod,
signalPeriod)
if array:
return macd, signal, hist
return macd[-1], signal[-1], hist[-1]
def onBar(self, bar):
if self.barHour != int(bar.datetime.hour):
self.barHour = int(bar.datetime.hour)
self.bar = VtBarData()
self.bar.open = bar.open
self.bar.high = bar.high
self.bar.low = bar.low
self.bar.close = bar.close
self.bar.datetime = bar.datetime
else:
self.bar.high = max(bar.high, self.bar.high)
self.bar.low = min(bar.low, self.bar.low)
self.bar.close = bar.close
return
if bar.close == self.closeArray[-1]:
self.no_data += 1
else:
self.no_data = 0
if self.no_data > 10: # 无数的周期过多时进行过滤
return
# print("initCapital {0}".format(self.initCapital))
# for orderID in self.orderList:
# self.cancelOrder(orderID)
# self.orderList = []
# 保存K线数据
self.closeArray[0:self.bufferSize -
1] = self.closeArray[1:self.bufferSize]
self.closeArray[-1] = bar.close
self.bufferCount += 1
if not self.init_data_loaded or self.bufferCount < self.slowMaLength:
return
# slow_arr = talib.MA(self.closeArray, self.slowMaLength)
# fast_arr = talib.MA(self.closeArray, self.fastMaLength)
macd, macdsignal, macdhist = talib.MACD(self.closeArray,
self.fast_period,
self.slow_period,
self.signal_period)
# slow_arr = talib.WMA(self.closeArray, self.slowMaLength)
# fast_arr = talib.WMA(self.closeArray, self.fastMaLength)
# slow_1 = slow_arr[-1]
# slow_2 = slow_arr[-2]
# fast_1 = fast_arr[-1]
# fast_2 = fast_arr[-2]
# 判断买卖
# crossOver = fast_1 > slow_1 and fast_2 <= slow_2 #and macd[-1] > macdsignal[-1] # 上穿
# crossBelow = fast_1 < slow_1 and fast_2 >= slow_2 #and macd[-1] < macdsignal[-1] # 下穿
crossOver = macd[-1] > macdsignal[-1] and macd[-2] <= macdsignal[
-2] # 上穿
crossBelow = macd[-1] < macdsignal[-1] and macd[-2] >= macdsignal[
-2] # 下穿
increasing = 0 # fast_1 > slow_1 # 快线在上
decreasing = 0 # fast_1 < slow_1 # 快线在下
volume = 0
self.cancelAll()
if self.ctaEngine.engineType == ENGINETYPE_TRADING:
# 加载没有完成成交的订单
incomplete_orders = self.ctaEngine.load_incomplete_orders(self)
for order in incomplete_orders:
if increasing and order['order_type'] == 3: # 平空
self.short(bar.close,
order['order_amount'] - order['deal_amount'])
elif decreasing and order['order_type'] == 4: # 平多
self.sell(bar.close,
order['order_amount'] - order['deal_amount'])
# account_right = self.ctaEngine.query_account().balance # btc 账户权益
account_right = self.ctaEngine.query_account().info[
self.vtSymbol[:3].lower()]['account_rights']
strategy_margin = self.margin_percent * account_right * bar.close # 用些策略的金额
else:
strategy_margin = self.initCapital
if self.pos == 0:
volume = strategy_margin / self.contract_size
if crossOver:
if self.stop == 0:
self.buy(bar.close, int(volume * 0.5))
else:
self.stop -= 1
elif crossBelow:
if self.stop == 0:
self.short(bar.close, int(volume * 2))
else:
self.stop -= 1
# 持有多头仓位
elif self.pos > 0:
sell_order = short_order = False
if self.last_entry_price > 0 and (
bar.close - self.last_entry_price
) / self.last_entry_price > self.stop_profit:
self.stop = 4
sell_order = True
# print("*"*20)
elif crossBelow:
sell_order = short_order = True
if sell_order:
self.sell(bar.close, abs(self.pos))
if self.ctaEngine.engineType != ENGINETYPE_TRADING:
self.initCapital = self.trade_result(bar)
strategy_margin = self.initCapital
volume = strategy_margin / self.contract_size
if short_order:
self.short(bar.close, int(volume * 2))
# 持有空头仓位
elif self.pos < 0:
cover_order = long_order = False
if self.last_entry_price > 0 and (
self.last_entry_price -
bar.close) / self.last_entry_price > self.stop_profit:
self.stop = 4
cover_order = True
# print("*" * 20)
elif crossOver:
cover_order = long_order = True
if cover_order:
self.cover(bar.close, abs(self.pos))
if self.ctaEngine.engineType != ENGINETYPE_TRADING:
self.initCapital = self.trade_result(bar)
strategy_margin = self.initCapital
volume = strategy_margin / self.contract_size
if long_order:
self.buy(bar.close, int(volume * 0.5))
# 发出状态更新事件
self.putEvent()
def getMACD(closePrices):
macd, macdsignal, macdhist = talib.MACD(closePrices, fastperiod=12, slowperiod=26, signalperiod=9)
return macd, macdsignal, macdhist
def mytest():
data = getStockallDataByCODE_mysql_step1(20150101, 20190921)
sector = data['3645']
sector = sector.set_index('DATE')
sector.index = pd.to_datetime(sector.index)
sector = sector.astype(float)
# sector = getDF(data['3645'],'close_price')
sector = sector.dropna()
# sector = data.get_group('3645')
# sector = sector.set_index('DATE')
# sector = sector.astype(float)
# close = sector.close_price
# sector= sector.dropna()
# sector.head()
# test.initData(sector.close_price.values)
# test.start()
dif, dea, macd = ta.MACD(sector.close_price.values,
fastperiod=12,
slowperiod=26,
signalperiod=9)
sector['dif'] = dif
sector['dea'] = dea
sector['macd'] = macd
sector.tail()
sector['close_price'].plot(figsize=(12, 4))
sector[['dif', 'dea']].plot(figsize=(12, 4))
sector[['macd']].plot(figsize=(12, 4), kind='bar', xticks=[], color='b')
plt.show()
sector.close_price.plot(figsize=(10, 5))
plt.ylabel("Gold ETF Prices")
# plt.show()
sector['S_3'] = sector['close_price'].shift(1).rolling(window=3).mean()
sector['S_9'] = sector['close_price'].shift(1).rolling(window=9).mean()
sector = sector.dropna()
X = sector[['S_3', 'S_9']]
X.head()
y = sector['close_price']
y.head()
t = .8
t = int(t * len(sector))
# Train dataset
X_train = X[:t]
y_train = y[:t]
# Test dataset
X_test = X[t:]
y_test = y[t:]
#
y_test2 = ['20190904']
linear = LinearRegression().fit(X_train, y_train)
print("Gold ETF Price =", round(linear.coef_[0],
2), "* 3 Days Moving Average",
round(linear.coef_[1], 2), "* 9 Days Moving Average +",
round(linear.intercept_, 2))
predicted_price = linear.predict(X_test)
predicted_price = pd.DataFrame(predicted_price,
index=y_test.index,
columns=['close_price'])
predicted_price.plot(figsize=(10, 5))
y_test.plot()
plt.legend(['predicted_price', 'actual_price'])
plt.ylabel("Gold ETF Price")
plt.show()
r2_score = linear.score(X[t:], y[t:]) * 100
r_squared = float("{0:.2f}".format(r2_score))
print(r_squared)
def add_macd(df):
close = np.array([float(x) for x in df['close']])
diff, dea, macd = talib.MACD(close, fastperiod=fastperiod, slowperiod=slowperiod, signalperiod=signalperiod)
df['DIFF'] = diff
df['MACD'] = macd
t_low = np.transpose(x[:, [2]])
t_vol = np.transpose(x[:, [3]])
t_y = np.transpose(y)
high = np.array(t_high[0], dtype='float')
low = np.array(t_low[0], dtype='float')
volume = np.array(t_vol[0], dtype='float')
close = np.array(t_y[0], dtype='float')
#-----------------------------------------
#-------------TA-Lib function 이용-----------------
b_upper, b_middle, b_lower = ta.BBANDS(close) #bollinger ban
ma = ta.MA(close, timeperiod=predict_len) #moving average
dmi = ta.DX(high, low, close, timeperiod=predict_len) #direct movement index
macd, macdsignal, macdhist = ta.MACD(
close, fastperiod=predict_len, slowperiod=predict_len * 2,
signalperiod=4) #moving average convergence/divergence
slowk, slowd = ta.STOCH(high,
low,
close,
fastk_period=predict_len,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0) #Stochastic
mom = ta.MOM(close, timeperiod=predict_len)
rsi = ta.RSI(close, timeperiod=predict_len * 2) #Relative Strength Index
cci = ta.CCI(high, low, close, timeperiod=predict_len * 2)
sma = ta.SMA(close, timeperiod=predict_len) #Simple Moving average
wma = ta.WMA(close, timeperiod=predict_len) #Weigth Moving average
def handle_data(context):
# 获取历史数据, 取后longest_history根bar
hist = context.data.get_price(context.security,
count=context.user_data.longest_history,
frequency="1d")
if len(hist.index) < context.user_data.longest_history:
context.log.warn("bar的数量不足, 等待下一根bar...")
return
# 历史收盘价
prices = np.array(hist["close"])
# 初始化买入卖出信号
long_signal_triggered = False
short_signal_triggered = False
try:
# talib计算MACD，返回三个数组，分别为DIF, DEA和MACD的值
macd_tmp = talib.MACD(prices,
fastperiod=context.user_data.short_window,
slowperiod=context.user_data.long_window,
signalperiod=context.user_data.macd_window)
# 获取MACD值
macd_hist = macd_tmp[2]
# 获取最新一个MACD的值
macd = macd_hist[-1]
context.log.info("当前MACD为: %s" % macd)
except:
context.log.error("计算MACD出错...")
return
# macd大于0时，产生买入信号
if macd > 0:
long_signal_triggered = True
# macd小于0时，产生卖出信号
elif macd < 0:
short_signal_triggered = True
# 有卖出信号，且持有仓位，则市价单全仓卖出
if short_signal_triggered:
context.log.info("MACD小于0，产生卖出信号")
if context.account.huobi_cny_btc >= HUOBI_CNY_BTC_MIN_ORDER_QUANTITY:
# 卖出信号，且不是空仓，则市价单全仓清空
context.log.info("正在卖出 %s" % context.security)
context.log.info("卖出数量为 %s" % context.account.huobi_cny_btc)
context.order.sell_limit(context.security,
quantity=str(
context.account.huobi_cny_btc),
price=str(prices[-1] * 0.98))
else:
context.log.info("仓位不足，无法卖出")
# 有买入信号，且持有现金，则市价单全仓买入
elif long_signal_triggered:
context.log.info("MACD大于0，产生买入信号")
if context.account.huobi_cny_cash >= HUOBI_CNY_BTC_MIN_ORDER_CASH_AMOUNT:
# 买入信号，且持有现金，则市价单全仓买入
context.log.info("正在买入 %s" % context.security)
context.log.info("下单金额为 %s 元" % context.account.huobi_cny_cash)
context.order.buy_limit(
context.security,
quantity=str(context.account.huobi_cny_cash / prices[-1] *
0.98),
price=str(prices[-1] * 1.02))
else:
context.log.info("现金不足，无法下单")
else:
context.log.info("无交易信号，进入下一根bar")
MACD_SIGNAL = 9
STOCH_K = 14
STOCH_D = 3
SIGNAL_TOL = 3
Y_AXIS_SIZE = 12
analysis = pd.DataFrame(index=sec_id['timestamp_datetime'])
analysis['sma_f'] = pd.rolling_mean(sec_id['close'], SMA_FAST)
analysis['sma_s'] = pd.rolling_mean(sec_id['close'], SMA_SLOW)
analysis['rsi'] = ta.RSI(sec_id['close'].as_matrix(), RSI_PERIOD)
analysis['sma_r'] = pd.rolling_mean(analysis.rsi,
RSI_AVG_PERIOD) # check shift
analysis['macd'], analysis['macdSignal'], analysis['macdHist'] = ta.MACD(
sec_id['close'].as_matrix(),
fastperiod=MACD_FAST,
slowperiod=MACD_SLOW,
signalperiod=MACD_SIGNAL)
analysis['stoch_k'], analysis['stoch_d'] = ta.STOCH(
sec_id['high'].as_matrix(),
sec_id['low'].as_matrix(),
sec_id['close'].as_matrix(),
slowk_period=STOCH_K,
slowd_period=STOCH_D)
analysis['sma'] = np.where(analysis.sma_f > analysis.sma_s, 1, 0)
analysis['macd_test'] = np.where((analysis.macd > analysis.macdSignal), 1, 0)
analysis['stoch_k_test'] = np.where(
(analysis.stoch_k < 50) & (analysis.stoch_k > analysis.stoch_k.shift(1)),
1, 0)
analysis['rsi_test'] = np.where(
stk_df = get_total_table_data(conn_k, 'ksh').loc[0:600, :]
# 计算均值
stk_df['close_5'] = stk_df['close'].rolling(window=5).mean()
stk_df['close_20'] = stk_df['close'].rolling(window=10).mean()
# 求20日均线导数
stk_df["M20_Diff"] = stk_df['close_20'] - stk_df['close_20'].shift(1)
stk_df["M20_Diff2"] = stk_df['M20_Diff'] - stk_df['M20_Diff'].shift(1)
stk_df['close_90'] = stk_df['close'].rolling(window=90).mean()
stk_df['C20-C90'] = stk_df['close_20'] - stk_df['close_90']
stk_df['20-90Diff'] = stk_df['C20-C90'] - stk_df['C20-C90'].shift(1)
stk_df['MACD'], stk_df['MACDsignal'], stk_df['MACDhist'] = talib.MACD(
stk_df.close, fastperiod=12, slowperiod=26, signalperiod=9)
stk_df['MACD_M5'], stk_df['MACDsignal_M20'], stk_df[
'MACDhist_M20'] = talib.MACD(stk_df.close_5,
fastperiod=12,
slowperiod=26,
signalperiod=9)
stk_df['RSI12'] = talib.RSI(stk_df.close, timeperiod=12)
# 将控制填0方便对其
stk_df = stk_df.fillna(0)
fig, ax = subplots(ncols=1, nrows=2)
ax[0].plot(stk_df['close'], 'g*--', label='close', linewidth=0.5, markersize=1)
def CalculateIndicators(startDate='', endDate=''):
client = MongoClient()
db = client.Project
stocks = [x['BBGTicker'] for x in list(db.Stocks.find({}))]
if startDate == '' and endDate == '':
db.HistSignals.delete_many({})
else:
d1 = datetime.strptime(startDate, '%Y-%m-%d')
d2 = datetime.strptime(endDate, '%Y-%m-%d')
delta = (d2 - d1).days + 1
for i in range(delta):
db.HistSignals.delete_many(
{'Date': (d1 + td(days=i)).strftime('%Y-%m-%d')})
for s in stocks:
print(s)
if startDate == '' and endDate == '':
data = GetDataSerieFromMongo(s)
else:
start = (datetime.strptime(startDate, '%Y-%m-%d') -
td(days=90)).strftime('%Y-%m-%d')
data = GetDataSerieFromMongo(s, start, endDate)
header = data[0].tolist()
header.append('SMA')
header.append('EMA')
header.append('KAMA')
header.append('ADX')
header.append('RSI')
header.append('CCI')
header.append('MACD')
header.append('MACDSIGNAL')
header.append('MACDHIST')
header.append('MFI')
header.append('AD')
header.append('ADOSCILLATOR')
header.append('ATR')
header.append('OBV')
header.append('STOCH')
header.append('MOM')
header.append('ROC')
header.append('BOLLINGERUPPER')
header.append('BOLLINGERMIDDLE')
header.append('BOLLINGERLOWER')
header.append('HILBERTTRENDLINE')
header.append('WILLIAMR')
if len(data) > 1:
closeP = numpy.array(data[1:, 1], dtype='f8')
#openP = numpy.array(data[1:,6], dtype='f8')
highP = numpy.array(data[1:, 3], dtype='f8')
lowP = numpy.array(data[1:, 4], dtype='f8')
volume = numpy.array(data[1:, 7], dtype='f8')
sma = talib.SMA(closeP, timeperiod=14)
ema = talib.EMA(closeP, timeperiod=30)
kama = talib.KAMA(closeP, timeperiod=30)
adx = talib.ADX(highP, lowP, closeP, timeperiod=14)
rsi = talib.RSI(closeP, timeperiod=14)
cci = talib.CCI(highP, lowP, closeP, timeperiod=14)
macd, signal, hist = talib.MACD(closeP,
fastperiod=12,
slowperiod=26,
signalperiod=9)
mfi = talib.MFI(highP, lowP, closeP, volume, timeperiod=14)
ad = talib.AD(highP, lowP, closeP, volume)
adOscillator = talib.ADOSC(highP,
lowP,
closeP,
volume,
fastperiod=3,
slowperiod=10)
atr = talib.ATR(highP, lowP, closeP, timeperiod=14)
obv = talib.OBV(closeP, volume)
slowk, slowd = talib.STOCH(highP,
lowP,
closeP,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
mom = talib.MOM(closeP, timeperiod=10)
roc = talib.ROC(closeP, timeperiod=10)
upperBB, middleBB, lowerBB = talib.BBANDS(closeP,
matype=MA_Type.T3)
hilbertTL = talib.HT_TRENDLINE(closeP)
williamR = talib.WILLR(highP, lowP, closeP, timeperiod=14)
res = numpy.c_[data[1:, :], sma, ema, kama, adx, rsi, cci, macd,
signal, hist, mfi, ad, adOscillator, atr, obv,
slowk, slowd, mom, roc, upperBB, middleBB, lowerBB,
hilbertTL, williamR]
records = res.tolist()
jsonDict = []
for item in records:
jsonDict.append(dict(zip(header, item)))
if startDate == '' and endDate == '':
db.HistSignals.insert_many(jsonDict)
else:
jsonDictFilter = [
a for a in jsonDict
if datetime.strptime(startDate, '%Y-%m-%d') <=
datetime.strptime(a['Date'], '%Y-%m-%d') <=
datetime.strptime(endDate, '%Y-%m-%d')
]
db.HistSignals.insert_many(jsonDictFilter)
print u"开始判断是否有突破"
a = [] #数组暂存收盘价用来接下来MACD生成
for i in range(1, 25):
a.append(n['data'][i]
['close']) #return float, this is close value,消去刚出现的价格
Open, Close, High, Low = n['data'][1]['open'], n['data'][1][
'close'], n['data'][1]['high'], n['data'][1]['low']
a.reverse() #此处翻转是为了下面的MACD生成
a = np.array(a)
real = talib.SMA(a, timeperiod=10) #EMA线
dif, dea, bar = talib.MACD(a,
fastperiod=6,
slowperiod=13,
signalperiod=6) #MACD 参数 6,13,6
if (bar[-2] < 0 and bar[-1] > 0) and (dif[-1] < 0 and dea[-1] < 0):
f = open('status.txt', 'r+')
status = float(f.readline()) #读取持仓状态,是否有其他币种的持仓
f.close()
if status == 0:
f = open('money.txt', 'r+')
zijin = float(f.readline()) #读取文本中设置的初始资金,模拟盘专用
f.close()
print "向上突破"
f = open('status.txt', 'w+')
f.write('1') #锁仓
f.close()
pp = tupo_1(float(real[-1]), Open, Close, High, Low,
# In[91]: x.shape # In[76]: events['side'].values # In[37]: help(talib.MACD) # In[42]: macd, signal, hist = talib.MACD(close.values) # In[45]: np.max(macd[100:] - signal[100:] - hist[100:]) # In[49]: macd[np.isfinite(macd)].shape # In[51]: signal = signal[np.isfinite(signal)] # In[55]:
