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# 啟動互動式繪圖環境
%pylab inline
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# 引入相依套件
import numpy as np
import pandas as pd
from numpy import random
import matplotlib.pyplot as plt
# 引入檔案
# 資料來源 3008 大立光 2012-8/1 ~ 2014-12/09
# http://www.twse.com.tw/ch/trading/exchange/STOCK_DAY/genpage/Report201412/201412_F3_1_8_3008.php?STK_NO=3008&myear=2014&mmon=12
datapath = '/Users/wy/Desktop/3008.txt'
data = pd.read_csv(datapath)
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# 看檔案前N筆
data.head(5)
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# data詳細資料 總數,平均數,標準差...
data.describe()
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# 技術分析資料來源
# http://hymar.myweb.hinet.net/study/stock/theory/
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# Rise Ratio 漲幅比
def RR(data):
# 由於 data 新到舊 0~xxx，遞增，因此需反轉陣列
dataList = range(data['Date'].size)
dataList.reverse()
tmpList = []
for item in dataList:
# 防止 第一筆data沒有更舊的
if item-1 >=0:
# (今日收盤價 - 昨日收盤價)/昨日收盤價
tmp = (data['Close'][item-1]-data['Close'][item])/data['Close'][item]*100
tmpList.append(tmp)
# 前day 沒data會出現NA
tmpList.reverse()
tmpSeries = pd.Series(tmpList)
# create RR 欄位
data['RR']=tmpSeries
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# 威廉指標(WMS%R或%R)
def WMS(data,day):
# 由於 data 新到舊 0~xxx，遞增，因此需反轉陣列
dataList = range(data['Date'].size)
dataList.reverse()
tmpList = []
for item in dataList:
# 防止前day沒有data
if item-day+1 >= 0:
# 9日WMS%R =(9日內最高價-第9日收盤價) / (9日內最高價-9日內最低價)*100
# [item-day+1:item+1] 今日區間 [item-day+1] 第N日 583-9=574+1=575
tmp = (data['High'][item-day+1:item+1].max()-data['Close'][item-day+1])/(data['High'][item-day+1:item+1].max()-data['Low'][item-day+1:item+1].min())*100
tmpList.append(tmp)
# 前day 沒data會出現NA
tmpList.reverse()
tmpSeries = pd.Series(tmpList)
# create WMS 欄位
data['WMS']=tmpSeries
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# 買賣意願指標 day 建議26
def BR(data,day):
# 由於 data 新到舊 0~xxx，遞增，因此需反轉陣列
dataList = range(data['Date'].size)
dataList.reverse()
tmpList = []
for item in dataList:
# 防止前day沒有data
if item-day >= 0:
# 26日BR = (今日最高價 - 昨日收盤價)26天累計總數 / (昨日收盤價 - 今日最低價)26天累計總數
# [(item-day+1)-1:(item+1)-1] 有-1 今日區間 [(item-day+1):(item+1)] 昨日區間
tmp = (data['High'][(item-day+1)-1:(item+1)-1].sum()-data['Close'][item-day+1:item+1].sum())/(data['Close'][item-day+1:item+1].sum()-data['Low'][(item-day+1)-1:(item+1)-1].sum())
tmpList.append(tmp)
# 前day 沒data會出現NA
tmpList.reverse()
tmpSeries = pd.Series(tmpList)
# create BR 欄位
data['BR']=tmpSeries
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# 買賣氣勢指標 day建議26
def AR(data,day):
# 由於 data 新到舊 0~xxx，遞增，因此需反轉陣列
dataList = range(data['Date'].size)
dataList.reverse()
tmpList = []
for item in dataList:
# 防止前day沒有data
if item-day+1 >= 0:
# 26日AR = (最高價 - 開盤價)26天累計總數 / (開盤價 - 最低價)26天累計總數
# [item-day+1:item+1] 今日區間
tmp = (data['High'][item-day+1:item+1].sum()-data['Open'][item-day+1:item+1].sum())/(data['Open'][item-day+1:item+1].sum()-data['Low'][item-day+1:item+1].sum())
tmpList.append(tmp)
# 前day 沒data會出現NA
tmpList.reverse()
tmpSeries = pd.Series(tmpList)
# create AR 欄位
data['AR']=tmpSeries
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# 平均成交量 mean volumn day建議12
def MV(data,day):
# 由於 data 新到舊 0~xxx，遞增，因此需反轉陣列
dataList = range(data['Date'].size)
dataList.reverse()
tmpList = []
for item in dataList:
# 防止前day沒有data
if item-day+1 >= 0:
# N日平均量 = N日內的成交量總和 / N
# [item-day+1:item+1] 今日區間
tmp = data['Volume'][item-day+1:item+1].mean()
tmpList.append(tmp)
# 前day 沒data會出現NA
tmpList.reverse()
tmpSeries = pd.Series(tmpList)
# create MV 欄位
data['MV']=tmpSeries
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# 移動平均線(MA，Moving Average) 建議12
def MA(data,day):
# 由於 data 新到舊 0~xxx，遞增，因此需反轉陣列
dataList = range(data['Date'].size)
dataList.reverse()
tmpList = []
for item in dataList:
# 防止前day沒有data
if item-day+1 >= 0:
# 移動平均數 = 採樣天數的股價合計 / 採樣天數
# [item-day+1:item+1] 今日區間
tmp = data['Close'][item-day+1:item+1].mean()
tmpList.append(tmp)
# 前day 沒data會出現NA
tmpList.reverse()
tmpSeries = pd.Series(tmpList)
# create MA 欄位
data['MA'+str(day)]=tmpSeries
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# 心理線(PSY) 建議13
def PSY(data,day):
# 由於 data 新到舊 0~xxx，遞增，因此需反轉陣列
dataList = range(data['Date'].size)
dataList.reverse()
tmpList = []
for item in dataList:
# 防止前day沒有data
if item-day >= 0:
# 13日PSY值 = ( 13日內之上漲天數 / 13 ) * 100
# [item-day+1-1:item+1-1] 跳一天 最早的天沒有RR值
count = 0
for a in data['RR'][item-day+1-1:item+1-1]:
if a > 0:
count+=1
tmp = float(count)/float(13)*100
tmpList.append(tmp)
# 前day 沒data會出現NA
tmpList.reverse()
tmpSeries = pd.Series(tmpList)
# create PSY 欄位
data['PSY']=tmpSeries
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# 能量潮(OBV) 建議12
def OBV(data,day):
# 由於 data 新到舊 0~xxx，遞增，因此需反轉陣列
dataList = range(data['Date'].size)
dataList.reverse()
tmpList = []
for item in dataList:
# 防止前day沒有data
if item-day >= 0:
# 今日OBV值 = 最近12天股價上漲日成交量總和 - 最近12天股價下跌日成交量總和
# 先由 ['RR'] 求出boolean值 > 0 True 套入['Volume']符合True全加起來
bolRise = data['RR'][item-day+1-1:item+1-1]>0
sumVolRise = data['Volume'][item-day+1-1:item+1-1][bolRise].sum()
bolDesc = data['RR'][item-day+1-1:item+1-1]<0
sumVolDesc = data['Volume'][item-day+1-1:item+1-1][bolDesc].sum()
tmp = sumVolRise-sumVolDesc
# 可切換 OBV累積12日移動平均值 = (最近12天股價上漲日成交量總和 - 最近12天股價下跌日成交量總和) / 12
# tmp = (sumVolRise-sumVolDesc)/12
tmpList.append(tmp)
# 前day 沒data會出現NA
tmpList.reverse()
tmpSeries = pd.Series(tmpList)
# create OBV 欄位
data['OBV']=tmpSeries
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# 數量指標(VR) 建議12
def VR(data,day):
# 由於 data 新到舊 0~xxx，遞增，因此需反轉陣列
dataList = range(data['Date'].size)
dataList.reverse()
tmpList = []
for item in dataList:
# 防止前day沒有data
if item-day >= 0:
# VR = ( N日內上漲日成交值總和 + 1/2*N日內平盤日成交值總和) / ( N日內下跌日成交值總和 + 1/2*N日內平盤日成交值總和)* 100％
# 先由 ['RR'] 求出boolean值 > 0 True 套入['Volume']符合True全加起來
bolRise = data['RR'][item-day+1-1:item+1-1]>0
sumVolRise = data['Volume'][item-day+1-1:item+1-1][bolRise].sum()
bolNorm = data['RR'][item-day+1-1:item+1-1] == 0
sumVolNorm = data['Volume'][item-day+1-1:item+1-1][bolNorm].sum()
bolDesc = data['RR'][item-day+1-1:item+1-1]<0
sumVolDesc = data['Volume'][item-day+1-1:item+1-1][bolDesc].sum()
tmp = (sumVolRise+0.5*sumVolNorm)/(sumVolDesc+0.5*sumVolNorm)*100
tmpList.append(tmp)
# 前day 沒data會出現NA
tmpList.reverse()
tmpSeries = pd.Series(tmpList)
# create VR 欄位
data['VR']=tmpSeries
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# 相對強弱指標(RSI) 建議6
def RSI(data,day):
# 由於 data 新到舊 0~xxx，遞增，因此需反轉陣列
dataList = range(data['Date'].size)
dataList.reverse()
tmpList = []
for item in dataList:
# 防止前day沒有data
if item-day >= 0:
# 6日RSI=100*6日內收盤上漲總幅度平均值 / (6日內收盤上漲總幅度平均值 - 6日內收盤下跌總幅度平均值)
# 先由 ['RR'] 求出boolean值 > 0 True 套入['Volume']符合True全加起來
bolRise = data['RR'][item-day+1-1:item+1-1]>0
meanRise = data['RR'][item-day+1-1:item+1-1][bolRise].mean()
bolDesc = data['RR'][item-day+1-1:item+1-1]<0
meanDesc = data['RR'][item-day+1-1:item+1-1][bolDesc].mean()
tmp = 100*meanRise/(meanRise-meanDesc)
tmpList.append(tmp)
# 前day 沒data會出現NA
tmpList.reverse()
tmpSeries = pd.Series(tmpList)
# create RSI 欄位
data['RSI']=tmpSeries
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# 乖離率(BIAS)
def BIAS(data,day):
# 由於 data 新到舊 0~xxx，遞增，因此需反轉陣列
dataList = range(data['Date'].size)
dataList.reverse()
tmpList = []
for item in dataList:
# 防止前day沒有data
if item-day+1 >= 0:
# N日乖離率 = (當日股價 - N日股價移動平均數) / N日平均股價
tmp = (data['Close'][item-day+1]-data['MA'+str(day)][item-day+1])/data['MA'+str(day)][item-day+1]*100
tmpList.append(tmp)
# 前day 沒data會出現NA
tmpList.reverse()
tmpSeries = pd.Series(tmpList)
# create BIAS 欄位
data['BIAS']=tmpSeries
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# RR漲幅比須先算出來，後續指標需用到此項
RR(data)
WMS(data,9)
BR(data,26)
AR(data,26)
MV(data,12)
MA(data,12)
# 算BIAS須先求出MA值
BIAS(data,12)
PSY(data,13)
OBV(data,12)
VR(data,12)
RSI(data,6)
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MA(data,20)
MA(data,60)
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data
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# Rise Ratio 漲幅比
# 0為最新資料 向左越來越新
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(data['RR'])
ax.set_title('RR')
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# 威廉指標(WMS%R或%R)
# 0為最新資料 向左越來越新
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(data['WMS'])
ax.set_title('WMS')
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# 買賣意願指標
# 0為最新資料 向左越來越新
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(data['BR'])
ax.set_title('BR')
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# 買賣氣勢指標
# 0為最新資料 向左越來越新
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(data['AR'])
ax.set_title('AR')
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# 平均成交量 mean volumn
# 0為最新資料 向左越來越新
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(data['MV'])
ax.set_title('MV')
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# 移動平均線(MA，Moving Average)
# 0為最新資料 向左越來越新
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(data['MA12'])
ax.set_title('MA12')
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# 乖離率(BIAS)
# 0為最新資料 向左越來越新
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(data['BIAS'])
ax.set_title('BIAS')
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# 心理線(PSY)
# 0為最新資料 向左越來越新
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(data['PSY'])
ax.set_title('PSY')
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# 能量潮(OBV)
# 0為最新資料 向左越來越新
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(data['OBV'])
ax.set_title('OBV')
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# 數量指標(VR)
# 0為最新資料 向左越來越新
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(data['VR'])
ax.set_title('VR')
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# 相對強弱指標(RSI)
# 0為最新資料 向左越來越新
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(data['RSI'])
ax.set_title('RSI')
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# MA20 和 MA60 比較 看出黃金交叉和死亡交叉
# 0為最新資料 向左越來越新
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(data['MA20'],color='#9D442F',label='MA20')
ax.plot(data['MA60'],color='#5D947E',label='MA60')
ax.legend(loc='best')
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