/
indicator.py
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/
indicator.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
calculate indicators
"""
import numpy as np
import pandas as pd
import log
logger = log.setlog("indicator.py")
def typ(df_prices_high, df_prices_low, df_prices_close):
"""
Typical Price = average( high + low + close )
"""
df_typ = (df_prices_high + df_prices_low + df_prices_close) / 3
df_typ.name = 'typ'
return df_typ
def ema(df_prices, i_period, i_start=1):
"""
Exponential Moving Average
S(t) = w*P(t) + (1-w) * S(t-1)
w = 2/(N+1)
"""
i_len = len(df_prices)
assert i_len >= i_period
df_ema = pd.ewma(df_prices, span=i_period, adjust=False, min_periods = i_start)
## copy value for datas before i_start
df_ema[:i_start-1] = df_prices[:i_start -1 ]
df_ema.name = 'ema' + str(i_period)
return df_ema
def sma(df_prices, i_period):
"""
Simple Moving Average
SMA[i] = (P[i-k+1] + ... + P[i]) / K
where K = i_period and P[i] is the most recent price
"""
i_len = len(df_prices)
assert i_len >= i_period
df_sma = pd.rolling_mean(df_prices,i_period)
## copy value for datas before i_period ( wait for enough data )
df_sma[:i_period -1] = df_prices[:i_period - 1]
df_sma.name = 'sma'+str(i_period)
return df_sma
def rsi(df_prices, i_period=14):
"""
Relative Strength Index
Up periods:
U = close(i) - close(i-1)
D = 0
Down period:
U = 0
D = close(i) - close(i-1)
RS = EMA(U) / EMA(D)
RSI = 100 - 100/(1+RS)
= EMA(U) / (EMA(U) + EMA(D)) *100
"""
i_len = len(df_prices)
assert i_len >= i_period
df_prices_U = df_prices - df_prices.shift(1)
df_prices_U[df_prices_U < 0] = 0
df_prices_U[0] = 0
df_prices_D = df_prices.shift(1) - df_prices
df_prices_D[df_prices_D < 0] = 0
df_prices_D[0] = 0
df_ema_U = ema(pd.Series(df_prices_U), i_period)
df_ema_D = ema(pd.Series(df_prices_D), i_period)
df_rsi = (df_ema_U / (df_ema_U + df_ema_D)) * 100
## set the first value
df_rsi[0] = 0.5
df_rsi = pd.Series(df_rsi, index=df_prices.index, name='rsi' + str(i_period))
return df_rsi
def cci(df_typ, df_c, i_period):
"""
http://en.wikipedia.org/wiki/Commodity_channel_index
CCI = (p - SMA(p)) / (σ(p) * 0.015)
p = typical price
SMA = simple moving average
σ = mean absolute deviation
"""
i_len = len(df_typ)
assert i_len >= i_period
df_mad = pd.rolling_apply(df_c,10,lambda x : np.fabs(x-x.mean()).mean())
df_sma = sma(df_c, i_period)
df_cci = ( df_typ - df_sma) / (df_mad * 0.015)
## set values before i_period ( wait for enough data )
df_cci[:i_period-1] = 0.
df_cci.name = 'cci' + str(i_period)
return df_cci
def tr(df_prices_high, df_prices_low, df_prices_close):
"""
True Range
TR=Max（︱high(i)-low(i) ︳，︳high(i)-close(i-1) ︳，︳low(i) - close(i-1) ︳）
"""
df_tr = pd.concat([df_prices_high - df_prices_low, abs(df_prices_high - df_prices_close.shift(1)),
abs(df_prices_low - df_prices_close.shift(1))], axis=1).max(axis=1)
df_tr[0] = df_prices_high[0] - df_prices_low[0]
df_tr = pd.Series(df_tr, index=df_prices_close.index, name='tr')
return df_tr
def kpi(df_prices):
"""
KPI = 3*EMA27 - 2* EMA50
"""
df_ema27 = ema(df_prices, 27, 27)
df_ema50 = ema(df_prices, 50, 50)
df_kpi = 3 * df_ema27 - 2 * df_ema50
df_kpi[:50] = df_prices[:50]
df_kpi.name = 'kpi'
return df_kpi
"""
def test(filepath):
df_prices = pd.read_csv(filepath, parse_dates=[0], index_col=0)
df_c = df_prices['close']
df_h = df_prices['high']
df_l = df_prices['low']
Typ = typ(df_h , df_l , df_c)
X50 = ema(df_c, 50)
X27 = ema(df_c, 27)
Rsi = rsi(df_c)
Cci10 = cci(Typ ,df_c, 10)
Tr = tr(df_h,df_l,df_c)
Kpi = kpi(df_c)
"""
if __name__ == "__main__":
try:
test('./ts/Stock/SH600000/SH600000_1D.ts')
except Exception,ex:
logger.error( "{0}: {1}".format(Exception,ex) )