def calculate_atr(self, window=14):
""" Average True Range Shows volatility of market
ATR(t) = ((n-1) * ATR(t-1) + Tr(t)) / n
where Tr(t) = Max(Abs(High - Low), Abs(High - Close(t - 1)), Abs(Low - Close(t - 1));
"""
self.set_max_win(window)
i = 0
tr_l = [0]
for i in range(self.df.index[-1]):
tr = max(self.df.get_value(i + 1, 'High'),
self.df.get_value(i, 'Adj_Close')) - min(self.df.get_value(i + 1, 'Low'),
self.df.get_value(i, 'Adj_Close'))
tr_l.append(tr)
name = 'atr_%s' % (window)
self.df[name] = pd.ewma(pd.Series(tr_l), span=window, min_periods=window)
return name
python类ewma()的实例源码
def calculate_trix(self, window=15):
""" Triple Exponential Moving Average Smooth the insignificant movements
TR(t) / TR(t-1) where
TR(t) = EMA(EMA(EMA(Price(t)))) over n days period
"""
self.set_max_win(window)
#ignore produced warnings for now #TODO
ema = pd.ewma(self.df['Adj_Close'], span=window, min_periods=window-1)
ema = pd.ewma(ema, span=window, min_periods=window-1)
ema = pd.ewma(ema, span=window, min_periods=window-1)
roc_l = [0]
for i in range(1, len(self.df.index) - 1):
roc_l.append((ema[i + 1] - ema[i]) / ema[i])
name = "trix_%s" % (window)
self.df[name] = pd.Series(roc_l)
return name
def RSI(df, n):
i = 0
UpI = [0]
DoI = [0]
while i + 1 <= df.index[-1]:
UpMove = df.get_value(i + 1, 'High') - df.get_value(i, 'High')
DoMove = df.get_value(i, 'Low') - df.get_value(i + 1, 'Low')
if UpMove > DoMove and UpMove > 0:
UpD = UpMove
else: UpD = 0
UpI.append(UpD)
if DoMove > UpMove and DoMove > 0:
DoD = DoMove
else: DoD = 0
DoI.append(DoD)
i = i + 1
UpI = pd.Series(UpI)
DoI = pd.Series(DoI)
PosDI = pd.Series(pd.ewma(UpI, span = n, min_periods = n - 1))
NegDI = pd.Series(pd.ewma(DoI, span = n, min_periods = n - 1))
RSI = pd.Series(PosDI / (PosDI + NegDI), name = 'RSI_' + str(n))
df = df.join(RSI)
return df
#True Strength Index
def main():
parser = argparse.ArgumentParser()
parser.add_argument('files', metavar='filename', type=str, nargs='*')
args = parser.parse_args()
args = vars(args)
files = args['files']
assert len(files) == 2
targets_df = pd.read_csv(files[0], header=0, index_col=False)
predict_df = pd.read_csv(files[1], header=0, index_col=False)
column = targets_df.columns[1]
targets = targets_df.as_matrix(columns=[column])
#predict_df[column] = pd.ewma(predict_df[column], com=1, adjust=False)
predictions = predict_df.as_matrix(columns=[column])
rmse, mse = calc_rmse(predictions, targets)
print("RMSE: %f, MSE: %f" % (rmse, mse))
moreIndicators.py 文件源码
项目：Stock-Market-Analysis-and-Prediction
作者: samshara
项目源码
文件源码
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def RSI(df, n):
i = 0
UpI = [0]
DoI = [0]
while i + 1 <= df.index[-1]:
UpMove = df.get_value(i + 1, 'High') - df.get_value(i, 'High')
DoMove = df.get_value(i, 'Low') - df.get_value(i + 1, 'Low')
if UpMove > DoMove and UpMove > 0:
UpD = UpMove
else: UpD = 0
UpI.append(UpD)
if DoMove > UpMove and DoMove > 0:
DoD = DoMove
else: DoD = 0
DoI.append(DoD)
i = i + 1
UpI = pd.Series(UpI)
DoI = pd.Series(DoI)
PosDI = pd.Series(pd.ewma(UpI, span = n, min_periods = n - 1))
NegDI = pd.Series(pd.ewma(DoI, span = n, min_periods = n - 1))
RSI = pd.Series(PosDI / (PosDI + NegDI), name = 'RSI_' + str(n))
df = df.join(RSI)
return df
#True Strength Index
def RSI(df, n):
i = 0
UpI = [0]
DoI = [0]
while i + 1 <= df.index[-1]:
UpMove = df.get_value(i + 1, 'High') - df.get_value(i, 'High')
DoMove = df.get_value(i, 'Low') - df.get_value(i + 1, 'Low')
if UpMove > DoMove and UpMove > 0:
UpD = UpMove
else: UpD = 0
UpI.append(UpD)
if DoMove > UpMove and DoMove > 0:
DoD = DoMove
else: DoD = 0
DoI.append(DoD)
i = i + 1
UpI = pd.Series(UpI)
DoI = pd.Series(DoI)
PosDI = pd.Series(pd.ewma(UpI, span = n, min_periods = n - 1))
NegDI = pd.Series(pd.ewma(DoI, span = n, min_periods = n - 1))
RSI = pd.Series(PosDI / (PosDI + NegDI), name = 'RSI_' + str(n))
df = df.join(RSI)
return df
#True Strength Index
def EMA(self, param):
return pd.ewma(param[0], span=param[1], adjust = True)
def MEMA(self, param):
return pd.ewma(param[0], span=param[1] * 2 - 1, adjust = False)
def SMA(self, param):
M = param[2]
if param[2] == 0:
M = 1
return pd.ewma(param[0], span = 2 * param[1] / M - 1)
def ema(f, c, p = 20):
r"""Calculate the mean on a rolling basis.
Parameters
----------
f : pandas.DataFrame
Dataframe containing the column ``c``.
c : str
Name of the column in the dataframe ``f``.
p : int
The period over which to calculate the rolling mean.
Returns
-------
new_column : pandas.Series (float)
The array containing the new feature.
References
----------
*An exponential moving average (EMA) is a type of moving average
that is similar to a simple moving average, except that more weight
is given to the latest data* [IP_EMA]_.
.. [IP_EMA] http://www.investopedia.com/terms/e/ema.asp
"""
new_column = pd.ewma(f[c], span=p)
return new_column
#
# Function maratio
#
def rolling_weighted_mean(series, window=200, min_periods=None):
min_periods = window if min_periods is None else min_periods
try:
return series.ewm(span=window, min_periods=min_periods).mean()
except BaseException:
return pd.ewma(series, span=window, min_periods=min_periods)
# ---------------------------------------------
def ATR(df, n = 20):
tr = TR(df)
ts_atr = pd.ewma(tr, span=n, min_periods = n-1, adjust = False)
ts_atr.name = 'ATR'+str(n)
return ts_atr
def TRIX(df, n):
EX1 = pd.ewma(df['close'], span = n, min_periods = n - 1, adjust = False)
EX2 = pd.ewma(EX1, span = n, min_periods = n - 1, adjust = False)
EX3 = pd.ewma(EX2, span = n, min_periods = n - 1, adjust = False)
return pd.Series(EX3/EX3.shift(1) - 1, name = 'Trix' + str(n))
#Average Directional Movement Index
def ADX(df, n):
return pd.Series(talib.ADX(df['high'].values, df['low'].values, df['close'].values, timeperiod = n), index = df.index, name = 'ADX_%s' % str(n))
# UpMove = df['high'] - df['high'].shift(1)
# DoMove = df['low'].shift(1) - df['low']
# UpD = pd.Series(UpMove)
# DoD = pd.Series(DoMove)
# UpD[(UpMove<=DoMove)|(UpMove <= 0)] = 0
# DoD[(DoMove<=UpMove)|(DoMove <= 0)] = 0
# ATRs = ATR(df,span = n, min_periods = n)
# PosDI = pd.Series(pd.ewma(UpD, span = n, min_periods = n - 1) / ATRs)
# NegDI = pd.Series(pd.ewma(DoD, span = n, min_periods = n - 1) / ATRs)
# ADX = pd.Series(pd.ewma(abs(PosDI - NegDI) / (PosDI + NegDI), span = n_ADX, min_periods = n_ADX - 1), name = 'ADX' + str(n) + '_' + str(n_ADX))
# return ADX
def MACD(df, n_fast, n_slow, n_signal):
EMAfast = pd.Series(pd.ewma(df['close'], span = n_fast, min_periods = n_slow - 1))
EMAslow = pd.Series(pd.ewma(df['close'], span = n_slow, min_periods = n_slow - 1))
MACD = pd.Series(EMAfast - EMAslow, name = 'MACD' + str(n_fast) + '_' + str(n_slow) + '_' + str(n_signal))
MACDsig = pd.Series(pd.ewma(MACD, span = n_signal, min_periods = n_signal - 1), name = 'MACDsig' + str(n_fast) + '_' + str(n_slow) + '_' + str(n_signal))
MACDhist = pd.Series(MACD - MACDsig, name = 'MACDhist' + str(n_fast) + '_' + str(n_slow) + '_' + str(n_signal))
return pd.concat([MACD, MACDsig, MACDhist], join='outer', axis=1)
def MassI(df):
Range = df['high'] - df['low']
EX1 = pd.ewma(Range, span = 9, min_periods = 8)
EX2 = pd.ewma(EX1, span = 9, min_periods = 8)
Mass = EX1 / EX2
MassI = pd.Series(pd.rolling_sum(Mass, 25), name = 'MassIndex')
return MassI
#Vortex Indicator
def TSI(df, r, s):
M = pd.Series(df['close'].diff(1))
aM = abs(M)
EMA1 = pd.Series(pd.ewma(M, span = r, min_periods = r - 1))
aEMA1 = pd.Series(pd.ewma(aM, span = r, min_periods = r - 1))
EMA2 = pd.Series(pd.ewma(EMA1, span = s, min_periods = s - 1))
aEMA2 = pd.Series(pd.ewma(aEMA1, span = s, min_periods = s - 1))
TSI = pd.Series(EMA2 / aEMA2, name = 'TSI' + str(r) + '_' + str(s))
return TSI
#Accumulation/Distribution
def Chaikin(df):
ad = (2 * df['close'] - df['high'] - df['low']) / (df['high'] - df['low']) * df['volume']
Chaikin = pd.Series(pd.ewma(ad, span = 3, min_periods = 2) - pd.ewma(ad, span = 10, min_periods = 9), name = 'Chaikin')
return Chaikin
#Money Flow Index and Ratio
def COPP(df, n):
M = df['close'].diff(int(n * 11 / 10) - 1)
N = df['close'].shift(int(n * 11 / 10) - 1)
ROC1 = M / N
M = df['close'].diff(int(n * 14 / 10) - 1)
N = df['close'].shift(int(n * 14 / 10) - 1)
ROC2 = M / N
Copp = pd.Series(pd.ewma(ROC1 + ROC2, span = n, min_periods = n), name = 'Copp' + str(n))
return Copp
#Keltner Channel
def FISHER(df, n, smooth_p = 0.7, smooth_i = 0.7):
roll_high = pd.rolling_max(df.high, n)
roll_low = pd.rolling_min(df.low, n)
price_loc = (df.close - roll_low)/(roll_high - roll_low) * 2.0 - 1
sm_price = pd.Series(pd.ewma(price_loc, com = 1.0/smooth_p - 1, adjust = False), name = 'FISHER_P')
fisher_ind = 0.5 * np.log((1 + sm_price)/(1 - sm_price))
sm_fisher = pd.Series(pd.ewma(fisher_ind, com = 1.0/smooth_i - 1, adjust = False), name = 'FISHER_I')
return pd.concat([sm_price, sm_fisher], join='outer', axis=1)
def TEMA(ts, n):
n = int(n)
ts_ema1 = pd.Series( pd.ewma(ts, span = n, adjust = False), name = 'EMA' + str(n) )
ts_ema2 = pd.Series( pd.ewma(ts_ema1, span = n, adjust = False), name = 'EMA2' + str(n) )
ts_ema3 = pd.Series( pd.ewma(ts_ema2, span = n, adjust = False), name = 'EMA3' + str(n) )
ts_tema = pd.Series( 3 * ts_ema1 - 3 * ts_ema2 + ts_ema3, name = 'TEMA' + str(n) )
return ts_tema
def EMA(df, n):
EMA = pd.Series(pd.ewma(df['Close'], span = n, min_periods = n - 1), name = 'EMA_' + str(n))
df = df.join(EMA)
return df
#Momentum
def ATR(df, n):
i = 0
TR_l = [0]
while i < df.index[-1]:
TR = max(df.get_value(i + 1, 'High'), df.get_value(i, 'Close')) - min(df.get_value(i + 1, 'Low'), df.get_value(i, 'Close'))
TR_l.append(TR)
i = i + 1
TR_s = pd.Series(TR_l)
ATR = pd.Series(pd.ewma(TR_s, span = n, min_periods = n), name = 'ATR_' + str(n))
df = df.join(ATR)
return df
#Bollinger Bands
def STO(df, n):
SOk = pd.Series((df['Close'] - df['Low']) / (df['High'] - df['Low']), name = 'SO%k')
SOd = pd.Series(pd.ewma(SOk, span = n, min_periods = n - 1), name = 'SO%d_' + str(n))
df = df.join(SOd)
return df
#Trix
def TRIX(df, n):
EX1 = pd.ewma(df['Close'], span = n, min_periods = n - 1)
EX2 = pd.ewma(EX1, span = n, min_periods = n - 1)
EX3 = pd.ewma(EX2, span = n, min_periods = n - 1)
i = 0
ROC_l = [0]
while i + 1 <= df.index[-1]:
ROC = (EX3[i + 1] - EX3[i]) / EX3[i]
ROC_l.append(ROC)
i = i + 1
Trix = pd.Series(ROC_l, name = 'Trix_' + str(n))
df = df.join(Trix)
return df
#Average Directional Movement Index
def ADX(df, n, n_ADX):
i = 0
UpI = []
DoI = []
while i + 1 <= df.index[-1]:
UpMove = df.get_value(i + 1, 'High') - df.get_value(i, 'High')
DoMove = df.get_value(i, 'Low') - df.get_value(i + 1, 'Low')
if UpMove > DoMove and UpMove > 0:
UpD = UpMove
else: UpD = 0
UpI.append(UpD)
if DoMove > UpMove and DoMove > 0:
DoD = DoMove
else: DoD = 0
DoI.append(DoD)
i = i + 1
i = 0
TR_l = [0]
while i < df.index[-1]:
TR = max(df.get_value(i + 1, 'High'), df.get_value(i, 'Close')) - min(df.get_value(i + 1, 'Low'), df.get_value(i, 'Close'))
TR_l.append(TR)
i = i + 1
TR_s = pd.Series(TR_l)
ATR = pd.Series(pd.ewma(TR_s, span = n, min_periods = n))
UpI = pd.Series(UpI)
DoI = pd.Series(DoI)
PosDI = pd.Series(pd.ewma(UpI, span = n, min_periods = n - 1) / ATR)
NegDI = pd.Series(pd.ewma(DoI, span = n, min_periods = n - 1) / ATR)
ADX = pd.Series(pd.ewma(abs(PosDI - NegDI) / (PosDI + NegDI), span = n_ADX, min_periods = n_ADX - 1), name = 'ADX_' + str(n) + '_' + str(n_ADX))
df = df.join(ADX)
return df
#MACD, MACD Signal and MACD difference
def MassI(df):
Range = df['High'] - df['Low']
EX1 = pd.ewma(Range, span = 9, min_periods = 8)
EX2 = pd.ewma(EX1, span = 9, min_periods = 8)
Mass = EX1 / EX2
MassI = pd.Series(pd.rolling_sum(Mass, 25), name = 'Mass Index')
df = df.join(MassI)
return df
#Vortex Indicator: http://www.vortexindicator.com/VFX_VORTEX.PDF
def TSI(df, r, s):
M = pd.Series(df['Close'].diff(1))
aM = abs(M)
EMA1 = pd.Series(pd.ewma(M, span = r, min_periods = r - 1))
aEMA1 = pd.Series(pd.ewma(aM, span = r, min_periods = r - 1))
EMA2 = pd.Series(pd.ewma(EMA1, span = s, min_periods = s - 1))
aEMA2 = pd.Series(pd.ewma(aEMA1, span = s, min_periods = s - 1))
TSI = pd.Series(EMA2 / aEMA2, name = 'TSI_' + str(r) + '_' + str(s))
df = df.join(TSI)
return df
#Accumulation/Distribution
def Chaikin(df):
ad = (2 * df['Close'] - df['High'] - df['Low']) / (df['High'] - df['Low']) * df['Volume']
Chaikin = pd.Series(pd.ewma(ad, span = 3, min_periods = 2) - pd.ewma(ad, span = 10, min_periods = 9), name = 'Chaikin')
df = df.join(Chaikin)
return df
#Money Flow Index and Ratio
def COPP(df, n):
M = df['Close'].diff(int(n * 11 / 10) - 1)
N = df['Close'].shift(int(n * 11 / 10) - 1)
ROC1 = M / N
M = df['Close'].diff(int(n * 14 / 10) - 1)
N = df['Close'].shift(int(n * 14 / 10) - 1)
ROC2 = M / N
Copp = pd.Series(pd.ewma(ROC1 + ROC2, span = n, min_periods = n), name = 'Copp_' + str(n))
df = df.join(Copp)
return df
#Keltner Channel
