def test_PLUS_DI(self):
class MyPLUS_DI(OperatorPLUS_DI):
def __init__(self, name, **kwargs):
super(MyPLUS_DI, self).__init__(100, name, **kwargs)
self.env.add_operator('minus_di', {
'operator': MyPLUS_DI,
})
string = 'minus_di(14, high, low, close)'
gene = self.env.parse_string(string)
self.assertRaises(IndexError, gene.eval, self.env, self.dates[98],
self.dates[-1])
df = gene.eval(self.env, self.dates[99], self.dates[100])
ser0, ser1 = df.iloc[0], df.iloc[1]
h = self.env.get_data_value('high').values
l = self.env.get_data_value('low').values
c = self.env.get_data_value('close').values
res0, res1, res = [], [], []
for i, val in ser0.iteritems():
res0.append(
talib.PLUS_DI(
h[:100, i], l[:100, i], c[:100,
i], timeperiod=14)[-1] == val)
for i, val in ser1.iteritems():
res0.append(
talib.PLUS_DI(h[1:100 + 1, i],
l[1:100 + 1, i],
c[1:100 + 1, i],
timeperiod=14)[-1] == val)
res.append(
talib.PLUS_DI(h[:100 + 1, i],
l[:100 + 1, i],
c[:100 + 1, i],
timeperiod=14)[-1] != val)
self.assertTrue(all(res0) and all(res1) and any(res))
def get(self, df):
h, l, c = df['h'], df['l'], df['c']
adx = talib.ADX(h, l, c, timeperiod=self.time_period)
plus_di = talib.PLUS_DI(h, l, c, timeperiod=self.time_period)
minus_di = talib.MINUS_DI(h, l, c, timeperiod=self.time_period)
last_adx, last_plus_di, last_minus_di = adx.iloc[-1], plus_di.iloc[-1], minus_di.iloc[-1]
material = dict(adx=adx, plus_di=plus_di, minus_di=minus_di)
if last_adx > self.trend_threshold:
if last_plus_di > last_minus_di:
indicator_value = IndicatorValue(ADXIndicatorSign.TREND_PLUS.name, material=material)
else:
indicator_value = IndicatorValue(ADXIndicatorSign.TREND_MINUS.name, material=material)
else:
if last_plus_di > last_minus_di:
indicator_value = IndicatorValue(ADXIndicatorSign.NO_TREND_PLUS.name, material=material)
else:
indicator_value = IndicatorValue(ADXIndicatorSign.NO_TREND_MINUS.name, material=material)
if not self.is_test:
logging.info('sign=>%s ADX=>%s +DI=>%s -DI=>%s', indicator_value.value, last_adx, last_plus_di,
last_minus_di)
return indicator_value
def dmi(self):
df = self.df
MINUS_DI = talib.MINUS_DI(df.high, df.low, df.close, timeperiod=14)
PLUS_DI = talib.PLUS_DI(df.high, df.low, df.close, timeperiod=14)
ADX = talib.ADX(df.high, df.low, df.close, timeperiod=6)
ADXR = talib.ADXR(df.high, df.low, df.close, timeperiod=6)
return PLUS_DI.tolist(), MINUS_DI.tolist(), ADX.tolist(), ADXR.tolist()
def record_ADX(context, data):
prices = data.history(context.stock, 'price', 252, '1d')
market = data.history(context.market, 'price', 252, '1d')
period = 14
H = data.history(context.stock, 'high', 2 * period, '1d').dropna()
L = data.history(context.stock, 'low', 2 * period, '1d').dropna()
C = data.history(context.stock, 'price', 2 * period, '1d').dropna()
ta_ADX = talib.ADX(H, L, C, period)
ta_nDI = talib.MINUS_DI(H, L, C, period)
ta_pDI = talib.PLUS_DI(H, L, C, period)
ADX = ta_ADX[-1]
nDI = ta_nDI[-1]
pDI = ta_pDI[-1]
beta = talib.BETA(prices, market, timeperiod=21)[-1:]
if (ADX > 25) and (nDI > pDI):
order_target_percent(context.stock, -1) and order_target_percent(
context.market, beta)
elif (ADX > 25) and (nDI < pDI):
order_target_percent(context.stock, 1) and order_target_percent(
context.market, -beta)
record(ADX=ADX, nDI=nDI, pDI=pDI)
def generate_feature(data):
high = data.High.values
low = data.Low.values
close = data.Close.values
feature_df = pd.DataFrame(index=data.index)
feature_df["ADX"] = ADX = talib.ADX(high, low, close, timeperiod=14)
feature_df["ADXR"] = ADXR = talib.ADXR(high, low, close, timeperiod=14)
feature_df["APO"] = APO = talib.APO(close, fastperiod=12, slowperiod=26, matype=0)
feature_df["AROONOSC"] = AROONOSC = talib.AROONOSC(high, low, timeperiod=14)
feature_df["CCI"] = CCI = talib.CCI(high, low, close, timeperiod=14)
feature_df["CMO"] = CMO = talib.CMO(close, timeperiod=14)
feature_df["DX"] = DX = talib.DX(high, low, close, timeperiod=14)
feature_df["MINUS_DI"] = MINUS_DI = talib.MINUS_DI(high, low, close, timeperiod=14)
feature_df["MINUS_DM"] = MINUS_DM = talib.MINUS_DM(high, low, timeperiod=14)
feature_df["MOM"] = MOM = talib.MOM(close, timeperiod=10)
feature_df["PLUS_DI"] = PLUS_DI = talib.PLUS_DI(high, low, close, timeperiod=14)
feature_df["PLUS_DM"] = PLUS_DM = talib.PLUS_DM(high, low, timeperiod=14)
feature_df["PPO"] = PPO = talib.PPO(close, fastperiod=12, slowperiod=26, matype=0)
feature_df["ROC"] = ROC = talib.ROC(close, timeperiod=10)
feature_df["ROCP"] = ROCP = talib.ROCP(close, timeperiod=10)
feature_df["ROCR100"] = ROCR100 = talib.ROCR100(close, timeperiod=10)
feature_df["RSI"] = RSI = talib.RSI(close, timeperiod=14)
feature_df["ULTOSC"] = ULTOSC = talib.ULTOSC(high, low, close, timeperiod1=7, timeperiod2=14, timeperiod3=28)
feature_df["WILLR"] = WILLR = talib.WILLR(high, low, close, timeperiod=14)
feature_df = feature_df.fillna(0.0)
matrix = np.stack((
ADX, ADXR, APO, AROONOSC, CCI, CMO, DX, MINUS_DI, ROCR100, ROC,
MINUS_DM, MOM, PLUS_DI, PLUS_DM, PPO, ROCP, WILLR, ULTOSC, RSI))
matrix = np.nan_to_num(matrix)
matrix = matrix.transpose()
return feature_df, matrix
def adx(prices_df: pd.DataFrame,
timeperiod=14) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
prices_arr = prices_df.to_numpy()
adxs, plus_dis, minus_dis = \
np.empty_like(prices_arr), np.empty_like(prices_arr), np.empty_like(prices_arr)
for col in range(prices_arr.shape[1]):
series = prices_arr[:, col]
adxs[:, col] = talib.ADX(high=series,
low=series,
close=series,
timeperiod=timeperiod)
plus_dis[:, col] = talib.PLUS_DI(high=series,
low=series,
close=series,
timeperiod=timeperiod)
minus_dis[:, col] = talib.MINUS_DI(high=series,
low=series,
close=series,
timeperiod=timeperiod)
return (pd.DataFrame(adxs,
dtype=prices_arr.dtype,
index=prices_df.index,
columns=prices_df.columns),
pd.DataFrame(plus_dis,
dtype=prices_arr.dtype,
index=prices_df.index,
columns=prices_df.columns),
pd.DataFrame(minus_dis,
dtype=prices_arr.dtype,
index=prices_df.index,
columns=prices_df.columns))
def plus_di(
client,
symbol,
timeframe="6m",
highcol="high",
lowcol="low",
closecol="close",
period=14,
):
"""This will return a dataframe of
Plus Directional Movement
for the given symbol across the given timeframe
Args:
client (pyEX.Client): Client
symbol (string): Ticker
timeframe (string): timeframe to use, for pyEX.chart
highcol (string): column to use to calculate
lowcol (string): column to use to calculate
closecol (string): column to use to calculate
period (int): period to calculate across
Returns:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
x = t.PLUS_DI(df[highcol].values, df[lowcol].values, df[closecol].values, period)
return pd.DataFrame(
{
highcol: df[highcol].values,
lowcol: df[lowcol].values,
closecol: df[closecol].values,
"plus_di": x,
}
)
def checkOne(code_str):
df = base.getOneStockData(code_str)
df['MINUS_DM_' + str(adx_timeperiod)] = ta.MINUS_DM(
np.array(df['high']), np.array(df['low']), timeperiod=adx_timeperiod)
df['PLUS_DM_' + str(adx_timeperiod)] = ta.PLUS_DM(
np.array(df['high']), np.array(df['low']), timeperiod=adx_timeperiod)
df['TR_' + str(adx_timeperiod)] = ta.TRANGE(np.array(df['high']),
np.array(df['low']),
np.array(df['close']))
df['MINUS_DI_' + str(adx_timeperiod)] = ta.MINUS_DI(
np.array(df['high']),
np.array(df['low']),
np.array(df['close']),
timeperiod=adx_timeperiod)
df['PLUS_DI_' + str(adx_timeperiod)] = ta.PLUS_DI(
np.array(df['high']),
np.array(df['low']),
np.array(df['close']),
timeperiod=adx_timeperiod)
df['ADX_' + str(adx_timeperiod)] = ta.ADX(np.array(df['high']),
np.array(df['low']),
np.array(df['close']),
timeperiod=adx_timeperiod)
df['ADXR_' + str(adx_timeperiod)] = ta.ADXR(np.array(df['high']),
np.array(df['low']),
np.array(df['close']),
timeperiod=adx_timeperiod)
df.to_csv(ADX_DIR + code_str + '.csv')
def compPLUSDI(self):
pdi = talib.PLUS_DI(self.high,self.low,self.close,timeperiod=self.lookback)
self.removeNullID(pdi)
self.rawFeatures['PLUS_DI']=pdi
FEATURE_SIZE_DICT['PLUS_DI'] = 1
return
def di(candles: np.ndarray, period=14, sequential=False) -> DI:
"""
DI - Directional Indicator
:param candles: np.ndarray
:param period: int - default=14
:param sequential: bool - default=False
:return: DI(plus, minus)
"""
if not sequential and len(candles) > 240:
candles = candles[-240:]
MINUS_DI = talib.MINUS_DI(candles[:, 3],
candles[:, 4],
candles[:, 2],
timeperiod=period)
PLUS_DI = talib.PLUS_DI(candles[:, 3],
candles[:, 4],
candles[:, 2],
timeperiod=period)
if sequential:
return DI(PLUS_DI, MINUS_DI)
else:
return DI(PLUS_DI[-1], MINUS_DI[-1])
def handle_data(context, data):
security = g.security
# 获取股票的价格信息
price = attribute_history(security, context.OBSERVATION, '1d', ('high','low','close'))
ADX = talib.ADX(price['high'].values,price['low'].values,price['close'].values, context.ADXPERIOD)
PDI = talib.PLUS_DI(price['high'].values,price['low'].values,price['close'].values, context.ADXPERIOD)
NDI = talib.MINUS_DI(price['high'].values,price['low'].values,price['close'].values, context.ADXPERIOD)
current_price = data[security].close
current_position = context.portfolio.positions[security].closeable_amount
cash = context.portfolio.cash
print(security)
if current_price !=0:
shares = cash/current_price
record(ADX=ADX[-1])
record(Plus_DI=PDI[-1])
record(Minus_DI=NDI[-1])
#ADX上行，+DI>-DI，当前空仓，则全仓买入标的
if ADX[-1]>ADX[-2] and PDI[-1]>NDI[-1] and current_position <= 0:
order_value(security,cash)
log.info("Buying %s" % (security))#记录交易信息
#ADX下行，+DI<-DI，则进行清仓
if ADX[-1]<ADX[-2] and PDI[-1]<NDI[-1] and current_position > 0:
order_target(security, 0)
log.info("Selling %s" % (security))
def ADX5_AROON(self, df):
adx5_exLevel = 95
adx5_brLevel = 10
df.drop(df.last_valid_index(), axis=0, inplace=True)
df['PLUS_DI'] = talib.PLUS_DI(df['High'].values, df['Low'].values, df['Close'].values, timeperiod=5)
df['MINUS_DI'] = talib.MINUS_DI(df['High'].values, df['Low'].values, df['Close'].values, timeperiod=5)
df['ADX5'] = talib.ADX(df['High'].values, df['Low'].values, df['Close'].values, timeperiod=5)
df['ARDN'], df['ARUP'] = talib.AROON(df['High'].values, df['Low'].values, timeperiod=10)
df['ADX5SHIFTED'] = df['ADX5'].shift(1)
df['ARDNSHIFTED'] = df['ARDN'].shift(1)
df['ARUPSHIFTED'] = df['ARUP'].shift(1)
columns = ['PLUS_DI', 'MINUS_DI', 'ADX5', 'AROONDN', 'AROONUP', 'ADX5SHIFTED', 'ARDNSHIFTED', 'ARUPSHIFTED']
def adx5_aroon_cond(row, columns):
df1_adx5_cond = (row['ADX5'] > row['ADX5SHIFTED']) & (row['ADX5'] > adx5_brLevel) & (
row['ADX5'] < adx5_exLevel)
arbuy = (row['ARUPSHIFTED'] < 5) & (row['ARUP'] > 85) & (row['ARUP'] > row['ARDN'])
arsell = (row['ARDNSHIFTED'] < 5) & (row['ARDN'] > 85) & (row['ARUP'] < row['ARDN'])
buy = (row['PLUS_DI'] > row['MINUS_DI']) & df1_adx5_cond & arbuy
sell = (row['PLUS_DI'] < row['MINUS_DI']) & df1_adx5_cond & arsell
return buy, sell
df['BUY'], df['SELL'] = zip(*df.apply(lambda row: adx5_aroon_cond(row, columns), axis=1))
df2 = df.tail(self.withinBars)
df2['BUY'].any()
df2['SELL'].any()
#return [df['SELL'].iloc[-1], df['BUY'].iloc[-1], df['normal_time']]
return [df2['SELL'].any(), df2['BUY'].any(), df['normal_time']]
def ADX(data, n=14):
"""
data: original data with open,high,low,close,vol
n: timeperiod
"""
data['adx'] = ta.ADX(
np.array(data.high), np.array(data.low), np.array(data.close),
n) #Average Directional Movement Index (Momentum Indicators)
data['mdi'] = ta.MINUS_DI(np.array(data.high), np.array(data.low),
np.array(data.close), n)
data['pdi'] = ta.PLUS_DI(np.array(data.high), np.array(data.low),
np.array(data.close), n)
signal = pd.DataFrame(index=data.index)
#strategy 1
"""
当+DI上穿-DI，买入，信号为1
当+DI下穿-DI，卖空，信号为-1
"""
signal['1'] = ((data['pdi'] > data['mdi']) &
(data['pdi'].shift(1) < data['mdi'].shift(1))) * 1 + (
(data['pdi'] <= data['mdi']) &
(data['pdi'].shift(1) < data['mdi'].shift(1))) * (-1)
signal['1'] = signal['1'][signal['1'].isin([1,
-1])].reindex(data.index,
method='ffill')
signal = signal.fillna(0)
return signal
def dmi(source, dmi_period, dmi_smooth): minus_name = "MINUS_DI_" + str(dmi_period) + "_" + str(dmi_smooth) plus_name = "PLUS_DI_" + str(dmi_period) + "_" + str(dmi_smooth) adx_name = "ADX_" + str(dmi_period) + "_" + str(dmi_smooth) source = source.join( pd.Series(talib.MINUS_DI(source['High'], source['Low'], source['Close'], timeperiod=dmi_period), name=minus_name)) source = source.join(pd.Series(talib.PLUS_DI(source['High'], source['Low'], source['Close'], timeperiod=dmi_period), name=plus_name)) source = source.join(pd.Series(talib.ADX(source['High'], source['Low'], source['Close'], timeperiod=dmi_smooth), name=adx_name)) # Trends source = do_uptrend(source, adx_name) source = do_uptrend(source, plus_name) source = do_uptrend(source, minus_name) # Overs source = do_over(source, adx_name, plus_name) source = do_over(source, adx_name, minus_name) source = do_over(source, plus_name, minus_name) # Aboves for level in range(5, 65, 5): source = do_above(source, adx_name, level) source = do_above(source, plus_name, level) source = do_above(source, minus_name, level) source = source.drop(columns=[adx_name, plus_name, minus_name]) return source
def __calculate_indicator(self):
# Create RSI Series
self.rsi = talib.RSI(self.numpy_array["close"], timeperiod=14)
# Create Bollinger Bands
bollinger_upper, bollinger_middle, bollinger_lower = talib.BBANDS(
self.numpy_array["close"], timeperiod=20)
self.bollinger_bandwidth = (
(bollinger_upper - bollinger_lower) / bollinger_middle) * 100
# Calculate Directional Indicators.
minus_dmi = talib.MINUS_DI(high=self.numpy_array["high"],
low=self.numpy_array["low"],
close=self.numpy_array["close"])
plus_dmi = talib.PLUS_DI(high=self.numpy_array["high"],
low=self.numpy_array["low"],
close=self.numpy_array["close"])
# If Directional Indicator gives Upward Direction.
self.dmi_difference = plus_dmi - minus_dmi
# Slope Calculation : Throws error if value is yet NaN
try:
self.rsi_slope_angle = self.__slope_to_degrees(
talib.LINEARREG_SLOPE(self.rsi, timeperiod=5))
self.bw_slope_angle = self.__slope_to_degrees(
talib.LINEARREG_SLOPE(self.bollinger_bandwidth, timeperiod=5))
except Exception:
#logging.info("Value of RSI and Bollinger is null")
raise StrategyNotReady(1, "Value of RSI and Bollinger is null")
return None
def get_strength_ADX(data):
op = np.array(talib.ADX(data['High'], data['Low'], data['Close']))
plus_DI = np.array(talib.PLUS_DI(data['High'], data['Low'], data['Close']))
minus_DI = np.array(
talib.MINUS_DI(data['High'], data['Low'], data['Close']))
#plots
list_traces = []
list_traces.append(go.Scatter(x=data['Date'], y=op, name='ADX'))
list_traces.append(go.Scatter(x=data['Date'], y=plus_DI, name='+DI'))
list_traces.append(go.Scatter(x=data['Date'], y=minus_DI, name='-DI'))
fig = go.Figure(data=list_traces)
plot(fig)
#score calculation
score = 0
if (op[-1] > 20):
if (plus_DI[-1] > minus_DI[-1]):
score = (op[-1] - 20) * 1.5
if score > 10:
score = 10
else:
score = (op[-1] - 20) * -1.5
if score < -10:
score = -10
return score, fig
def plus_di(df, periods=14):
df, data = reset_index(df)
df['DI+'] = ta.PLUS_DI(df['High'],
df['Low'],
df['Close'],
timeperiod=periods)
return df
def strategy_ema(candles, outperiod):
# Define the decision period - in minutes
decision_period=outperiod
# Convert candles to decision period
outcandles=convert_candle(candles,decision_period)
# Calculate indicators - EMA, DMI
short_ema=talib.EMA(outcandles[:,3], timeperiod=9)
long_ema=talib.EMA(outcandles[:,3], timeperiod=20)
ema_diff=short_ema-long_ema
plus_di=talib.PLUS_DI(outcandles[:,4], outcandles[:,1], outcandles[:,3], timeperiod=14) # PLUS_DI(high, low, close, timeperiod=14)
minus_di=talib.MINUS_DI(outcandles[:,4], outcandles[:,1], outcandles[:,3], timeperiod=14) #MINUS_DI(high, low, close, timeperiod=14)
dm_diff=plus_di-minus_di
# Iterate over the candles and determine buy/sell
decision_table=[]
for i in range(len(outcandles)):
if math.isnan(ema_diff[i]) or math.isnan(dm_diff[i]):
decision_table.append("") # do nothing
else:
decision_table.append("")
if ema_diff[i]>0 and ema_diff[i-1]<0: # if crossed from below, buys
#if dm_diff[i]>0: # confirm with directional movement
decision_table[i]="buy"
elif ema_diff[i]<0 and ema_diff[i-1]>0: #if cross from above, sells
#if dm_diff[i]<0: # confirm with directional movement
decision_table[i]="sell"
# Check if it has crossed the ema_diff
return decision_table
def get_DI_positive(self, p=14):
s = "DI_plus_{}".format(str(p))
if s not in self.ti_df.columns:
self.ti_df[s] = talib.PLUS_DI(self.history_df['High'],
self.history_df['Low'],
self.history_df['Close'], p)
return self.ti_df[s]
def handle_bar(self, ctx):
code = 'JCP'
adx_period = 14
short_ma_num = 5
long_ma_num = 20
data = ctx.data_provider.load_previous_quotes(
code, end_date=ctx.current_date, previous_num=100)
if len(data.date) == 0:
return
data_high = data.high[:-1]
data_low = data.low[:-1]
data_close = data.close[:-1]
adx = talib.ADX(data_high, data_low, data_close, adx_period)
plus_di = talib.PLUS_DI(data_high, data_low, data_close, adx_period)
minus_di = talib.MINUS_DI(data_high, data_low, data_close, adx_period)
short_ma = talib.SMA(data_close, short_ma_num)
long_ma = talib.SMA(data_close, long_ma_num)
current_price = data.close[-1]
ma_up_cross = short_ma[-1] > long_ma[-1] and short_ma[-2] < long_ma[-2]
ma_down_cross = short_ma[-1] < long_ma[-1] and short_ma[-2] > long_ma[
-2]
adx_up = adx[-1] > adx[-2]
di_up = plus_di[-1] > minus_di[-1]
if ma_up_cross and adx_up and di_up:
ctx.order(code, 0.99, current_price)
if ma_down_cross and not adx_up and not di_up:
ctx.order(code, 0, current_price)
def dmi(source, dmi_period, dmi_smooth):
minus_name = "MINUS_DI_" + str(dmi_period) + "_" + str(dmi_smooth)
plus_name = "PLUS_DI_" + str(dmi_period) + "_" + str(dmi_smooth)
adx_name = "ADX_" + str(dmi_period) + "_" + str(dmi_smooth)
source = source.join(
pd.Series(talib.MINUS_DI(source['High'],
source['Low'],
source['Close'],
timeperiod=dmi_period),
name=minus_name))
source = source.join(
pd.Series(talib.PLUS_DI(source['High'],
source['Low'],
source['Close'],
timeperiod=dmi_period),
name=plus_name))
source = source.join(
pd.Series(talib.ADX(source['High'],
source['Low'],
source['Close'],
timeperiod=dmi_smooth),
name=adx_name))
return source
def di(candles: np.ndarray, period: int = 14, sequential: bool = False) -> DI:
"""
DI - Directional Indicator
:param candles: np.ndarray
:param period: int - default: 14
:param sequential: bool - default: False
:return: DI(plus, minus)
"""
candles = slice_candles(candles, sequential)
MINUS_DI = talib.MINUS_DI(candles[:, 3],
candles[:, 4],
candles[:, 2],
timeperiod=period)
PLUS_DI = talib.PLUS_DI(candles[:, 3],
candles[:, 4],
candles[:, 2],
timeperiod=period)
if sequential:
return DI(PLUS_DI, MINUS_DI)
else:
return DI(PLUS_DI[-1], MINUS_DI[-1])
def dmi(candles: np.ndarray, period=14, sequential=False) -> DMI:
"""
DMI - Directional Movement Index
:param candles: np.ndarray
:param period: int - default=14
:param sequential: bool - default=False
:return: float | np.ndarray
"""
if not sequential and len(candles) > 240:
candles = candles[-240:]
MINUS_DI = talib.MINUS_DI(candles[:, 3],
candles[:, 4],
candles[:, 2],
timeperiod=period)
PLUS_DI = talib.PLUS_DI(candles[:, 3],
candles[:, 4],
candles[:, 2],
timeperiod=period)
if sequential:
return DMI(PLUS_DI, MINUS_DI)
else:
return DMI(PLUS_DI[-1], MINUS_DI[-1])
def momentum(self): adx = talib.ADX(self.high,self.low,self.close,self.period) adxr = talib.ADXR(self.high,self.low,self.close,self.period) apo = talib.APO(self.high,self.low,self.close,self.period) aroondown, aroonup = talib.AROON(self.high, self.low, period) aroonosc = talib.AROONOSC(self.high,self.low,self.period) bop = talib.BOP(self.opens,self.high,self.low,self.close) cci = talib.CCI(self.high,self.low,self.close,self.period) cmo = talib.CMO(self.close,self.period) dx = talib.DX(self.high,self.low,self.close,self.period) macd, macdsignal, macdhist = talib.MACD(self.close, fastperiod=period, slowperiod=period*5, signalperiod=period*2) macd1, macdsignal1, macdhist1 = talib.MACDEXT(self.close, fastperiod=12, fastmatype=0, slowperiod=26, slowmatype=0, signalperiod=9, signalmatype=0) macd2, macdsignal2, macdhist2 = talib.MACDFIX(self.close, signalperiod=9) mfi = talib.MFI(self.high, self.low, self.close, self.volume, timeperiod=14) minus_di = talib.MINUS_DI(self.high, self.low, self.close, timeperiod=14) minus_dm = talib.MINUS_DM(self.high, self.low, timeperiod=14) mom = talib.MOM(self.close, timeperiod=10) plus_di = talib.PLUS_DI(self.high, self.low, self.close, timeperiod=14) plus_dm = talib.PLUS_DM(self.high, self.low, timeperiod=14) ppo = talib.PPO(self.close, fastperiod=12, slowperiod=26, matype=0) roc = talib.ROC(self.close, timeperiod=10) rocp = talib.ROCP(self.close, timeperiod=10) rocr = talib.ROCR(self.close, timeperiod=10) rocr100 = talib.ROCR100(self.close, timeperiod=10) rsi = talib.RSI(self.close, timeperiod=14) slowk, slowd = talib.STOCH(self.high, self.low, self.close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0) fastk, fastd = talib.STOCHF(self.high, self.low, self.close, fastk_period=5, fastd_period=3, fastd_matype=0) fastk1, fastd1 = talib.STOCHRSI(self.close, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0) trix = talib.TRIX(self.close, timeperiod=30) ultosc = talib.ULTOSC(self.high, self.low, self.close, timeperiod1=7, timeperiod2=14, timeperiod3=28) willr = talib.WILLR(self.high, self.low, self.close, timeperiod=14)
def record_ADX(context, data):
period = 14
H = data.history(context.aapl,'high', 2*period, '1d').dropna()
L = data.history(context.aapl,'low', 2*period, '1d').dropna()
C = data.history(context.aapl,'price', 2*period, '1d').dropna()
ta_ADX = talib.ADX(H, L, C, period)
ta_nDI = talib.MINUS_DI(H, L, C, period)
ta_pDI = talib.PLUS_DI(H, L, C, period)
ADX = ta_ADX[-1]
nDI = ta_nDI[-1]
pDI = ta_pDI[-1]
record( ADX = ADX, nDI = nDI, pDI = pDI)
record(close_price = data.current(context.aapl,'close'))
if nDI < pDI and ADX > 20 and data.can_trade(context.aapl):
order_target_percent(context.aapl, 1)
elif nDI > pDI and ADX > 20 and data.can_trade(context.aapl):
order_target_percent(context.aapl, -1)
print context.portfolio.positions[context.aapl].amount
def ADX_Overlap_old(self, args):
adx5_exLevel = 70
adx5_brLevel = 25
adx14_exLevel = 45
adx14_brLevel = 15
df = args.df_current
df.drop(df.last_valid_index(), axis=0, inplace=True)
df['PLUS_DI'] = talib.PLUS_DI(df['High'].values,
df['Low'].values,
df['Close'].values,
timeperiod=5)
df['MINUS_DI'] = talib.MINUS_DI(df['High'].values,
df['Low'].values,
df['Close'].values,
timeperiod=5)
df['ADX5'] = talib.ADX(df['High'].values,
df['Low'].values,
df['Close'].values,
timeperiod=5)
df['ADX14'] = talib.ADX(df['High'].values,
df['Low'].values,
df['Close'].values,
timeperiod=14)
df['ADX5_Cond'] = (df.ADX5 > df.ADX5.shift(1)) & (
df.ADX5 > adx5_brLevel) & (df.ADX5 < adx5_exLevel)
df['ADX14_Cond'] = (df.ADX14 > df.ADX14.shift(1)) & (
df.ADX14 > adx14_brLevel) & (df.ADX14 < adx14_exLevel)
df['ADX5_14_OL'] = df['ADX5_Cond'] & df['ADX14_Cond']
df['BUY'] = (df['PLUS_DI'] > df['MINUS_DI']) & df['ADX5_14_OL']
df['SELL'] = (df['PLUS_DI'] < df['MINUS_DI']) & df['ADX5_14_OL']
return [df['SELL'].iloc[-1], df['BUY'].iloc[-1]]
def plus_di(self, n, array=False):
"""
PLUS_DI.
"""
result = talib.PLUS_DI(self.high, self.low, self.close, n)
if array:
return result
return result[-1]
def dmi(self, df, index):
MINUS_DI = talib.MINUS_DI(df.high, df.low, df.close, timeperiod=14)
#DX = talib.DX(df.high,df.low,df.close,timeperiod=14)
PLUS_DI = talib.PLUS_DI(df.high, df.low, df.close, timeperiod=14)
#PLUS_DM = talib.PLUS_DM(df.high,df.low, timeperiod=14)
ADX = talib.ADX(df.high, df.low, df.close, timeperiod=6)
ADXR = talib.ADXR(df.high, df.low, df.close, timeperiod=6)
return MINUS_DI, PLUS_DI, ADX, ADXR
def plus_di(self, n: int, array: bool = False) -> Union[float, np.ndarray]:
"""
PLUS_DI.
"""
result = talib.PLUS_DI(self.high, self.low, self.close, n)
if array:
return result
return result[-1]
def add_plusdi_ft(df, daily_count, count):
for idx in range(1, count + 1):
col_name = 'PLUSDI_' + str(idx)
df[col_name] = talib.PLUS_DI(df.high,
df.low,
df.close,
timeperiod=daily_count * idx)
return df
