##########################################################################################
# Technical Analysis Data
# MACD, RSI, SMA, EMA
# 技術指標來源： https://matplotlib.org/examples/pylab_examples/finance_work2.html
##########################################################################################
def getTicker():
import sqlite3
dbName = "list.db"
tableName = "StockList"
conn = sqlite3.connect(dbName)
c = conn.cursor()
sqlquery = "SELECT code, type FROM " + str(tableName)
out = conn.execute(sqlquery)
stockList = []
for code, typ in out:
stockList.append([code, typ])
conn.close()
return stockList
def getData(ticker):
import sqlite3
dbName = "PriceVolData.db"
tableName = "twse"
conn = sqlite3.connect(dbName)
c = conn.cursor()
sqlquery = "SELECT code, close, date FROM twse WHERE code=" + str(ticker)
out = conn.execute(sqlquery)
dalist = []
for code, close, date in out:
dalist.append([code, close, date[:10]])
conn.close()
return dalist
def moving_average(x, n, type='simple'):
from numpy import array
import numpy as np
#compute an n period moving average.
#type is 'simple' | 'exponential'
x = np.asarray(x)
if type == 'simple':
weights = np.ones(n)
else:
weights = np.exp(np.linspace(-1., 0., n))
weights /= weights.sum()
a = np.convolve(x, weights, mode='full')[:len(x)]
a[:n] = a[n]
return a
def moving_average_convergence(x, nslow=26, nfast=12):
from numpy import array
import numpy as np
#compute the MACD (Moving Average Convergence/Divergence) using a fast and slow exponential moving avg'
#return value is emaslow, emafast, macd which are len(x) arrays
emaslow = moving_average(x, nslow, type='exponential')
emafast = moving_average(x, nfast, type='exponential')
return emaslow, emafast, emafast - emaslow
def relative_strength(prices, n=14):
from numpy import array
import numpy as np
#compute the n period relative strength indicator
#http://stockcharts.com/school/doku.php?id=chart_school:glossary_r#relativestrengthindex
#http://www.investopedia.com/terms/r/rsi.asp
deltas = np.diff(prices)
seed = deltas[:n+1]
up = seed[seed >= 0].sum()/n
down = -seed[seed < 0].sum()/n
rs = up/down
rsi = np.zeros_like(prices)
rsi[:n] = 100. - 100./(1. + rs)
for i in range(n, len(prices)):
delta = deltas[i - 1] # cause the diff is 1 shorter
if delta > 0:
upval = delta
downval = 0.
else:
upval = 0.
downval = -delta
up = (up*(n - 1) + upval)/n
down = (down*(n - 1) + downval)/n
rs = up/down
rsi[i] = 100. - 100./(1. + rs)
return rsi
def getTA(dalist, nslow=20, nfast=5, nema=10):
from numpy import array
import numpy as np
fh = array(dalist) # Data(Ticker,Price,Date) to Numpy Array
prices = fh[:,1].astype(np.float) # Price from Str to float
emaslow, emafast, macd = moving_average_convergence(prices, nslow=nslow, nfast=nfast)
rsi = relative_strength(prices) # RSI
revMACD = list(reversed(macd))
revRSI = list(reversed(rsi))
revDalist = list(reversed(dalist))
dalistTA = []
for i in range(len(dalist)):
dalistTA.append([dalist[i][0],dalist[i][1],dalist[i][2],revMACD[i],revRSI[i]])
reversed(dalistTA)
return dalistTA