def main(df):
def call(row):
if row["adx"] > 30 \
and row["diff"] < 1.0 \
and row["diff_shift1"] < 1.0 \
and row["diff_shift2"] < 1.005 :
return 1
return 0
df.reset_index(drop=True, inplace=True)
df["adx"] = pta.ADX(df, 14, 7)
df["diff"] = df.close / df.close.shift(1)
df["diff_shift1"] = df["diff"].shift(1)
df["diff_shift2"] = df["diff"].shift(2)
df["diff_shift3"] = df["diff"].shift(3)
df["ta_sig_123recall"] = df.apply(lambda row: call(row), axis=1)
del df["adx"]
del df["diff"]
del df["diff_shift1"]
del df["diff_shift2"]
del df["diff_shift3"]
return df
def main(df):
highs = df['high'].values
lows = df['low'].values
closes = df['close'].values
df = df.join(pta.MDI(df, span))
df = df.join(pta.PDI(df, span))
df = df.join(pta.ADX(df, span, span))
df["cross_%d" % span] = df["ta_PDI_%d" % span] / df["ta_MDI_%d" % span]
df["cross_%d_shift_1" % span] = df["cross_%d" % span].shift(1)
df["ta_sig_adx"] = df.apply(lambda row: adx_signal(row), axis=1)
del df["ta_MDI_%d" % span]
del df["ta_PDI_%d" % span]
del df["ta_ADX_%d_%d" % (span, span)]
del df["cross_%d" % span]
del df["cross_%d_shift_1" % span]
return df
def main(df):
def call(row):
if row["adx"] > 40 and row["is_in"] > 0 and row["is_in_shift1"] > 0:
return 1.0
return 0.0
def call_in(row):
if row['high'] > row['high_shift1'] and row['low'] < row['low_shift1']:
return 1
return 0
df.reset_index(drop=True, inplace=True)
df["high_shift1"] = df["high"].shift(1)
df["low_shift1"] = df["low"].shift(1)
df["is_in"] = df.apply(lambda row: call_in(row), axis=1)
df["is_in_shift1"] = df["low"].shift(1)
df["adx"] = pta.ADX(df, 14, 14)
df["ta_sig_spoon"] = df.apply(lambda row: call(row), axis=1)
return df
def sig_adx(df):
highs = df['high'].values
lows = df['low'].values
closes=df['close'].values
df['ta_NATR_%d' % 14] = talib.NATR(highs, lows, closes, 14)
df = df.join(pta.MDI(df, span))
df = df.join(pta.PDI(df, span))
df = df.join(pta.ADX(df, span, span))
df["cross_%d" % span] = df["ta_PDI_%d" % span] / df["ta_MDI_%d" % span]
df["cross_%d_shift_1" % span ] = df["cross_%d" % span].shift(1)
df["ta_adx_signal_%d" % span] = df.apply(lambda row: adx_signal(row), axis = 1)
del df["ta_MDI_%d" % span]
del df["ta_PDI_%d" % span]
del df["ta_ADX_%d_%d" % (span,span)]
del df["cross_%d" % span]
del df["cross_%d_shift_1" % span]
return df