繪製BBand與爬蟲
--- 技術指標用的好，也要批量選股，我們先來看初步繪圖成果吧新增了選擇檔案，稍微改了一下介面，等等批量股票篩選（暫不小心用太多執行續被官網鎖IPQQ
BBAND.py
BBand=get_BBand()
BBand_up=[float(x) for x in BBand [0]]
BBand_sma=[float(x) for x in BBand [1]]
BBand_down=[float(x) for x in BBand [2]]
BBand_mid=[]
BBand_width=[]
for x in range(0,len(BBand_sma),1):
BBand_mid.append((BBand_up[x]+BBand_down[x])/2)
for x in range(0,len(BBand_mid),1):
BBand_width.append(100*((BBand_up[x]-BBand_down[x])/BBand_mid[x]))
爬蟲與篩選
#近三個月 bb寬度篩選
# 建立 5 個子執行緒
threads = []
for x in range (1000,9000,10):
for i in range(10):
threads.append(threading.Thread(target = job, args = (x+i,)))
threads[i].start()
print (i)
# 等待所有子執行緒結束
for i in range(10):
threads[i].join()
print (x+i)
threads.clear()
# 子執行緒的工作函數
def job(num):
# global catch_stock
# tmp=''
# if(get_historical_data(str(num)+'.TW', 90)>20):
# catch_stock.append(str(num)+'.TW')
# tmp=str(num)+'.TW'
print (get_historical_data(str(num)+'.TW', 90))
def get_historical_data(name, number_of_days):
max_bb=0
data = []
url = "https://finance.yahoo.com/quote/" + name + "/history/"
try:
rows = BeautifulSoup(urllib.request.urlopen(url).read()).findAll('table')[0].tbody.findAll('tr')
for each_row in rows:
divs = each_row.findAll('td')
if divs[1].span.text != 'Dividend': #Ignore this row in the table
#I'm only interested in 'Open' price; For other values, play with divs[1 - 5]
date_process=divs[0].span.text.replace(",", "")
date_process=datetime.strptime(date_process, '%b %d %Y').date().strftime('%Y/%m/%d')
t = date_process.replace('/','-')
date_process = (dates.date2num(datetime.strptime(t, '%Y-%m-%d')))
#date_process=datetime.datetime.strptime(date_process, '%b %d %Y').date().strftime('%m %d %Y')
#data.append({'Date': str(date_process), 'Open': float(divs[1].span.text.replace(',',''))\
# , 'High': float(divs[2].span.text.replace(',',''))\
# , 'Low': float(divs[3].span.text.replace(',',''))\
# , 'Close': float(divs[4].span.text.replace(',',''))\
# , 'Volume': float(divs[6].span.text.replace(',',''))})
#datas2 = (dates.date2num(datetime.strptime(t, '%Y-%m-%d')) , ope[x], high[x], low[x],close[x],vol[x])#
data.append([ str(date_process), float(divs[1].span.text.replace(',',''))\
, float(divs[2].span.text.replace(',',''))\
, float(divs[3].span.text.replace(',',''))\
, float(divs[4].span.text.replace(',',''))\
, float(divs[6].span.text.replace(',',''))])
#篩選指標
date.clear()
low.clear()
high.clear()
close.clear()
ope.clear()
vol.clear()
for x in data:
date.append(x[0])
ope.append(x[1])
high.append(x[2])
low.append(x[3])
close.append(x[4])
vol.append(x[5])
date.reverse()
low.reverse()
high.reverse()
close.reverse()
ope.reverse()
vol.reverse()
BBand=get_BBand()
BBand_up=[float(x) for x in BBand [0]]
BBand_sma=[float(x) for x in BBand [1]]
BBand_down=[float(x) for x in BBand [2]]
BBand_mid=[]
BBand_width=[]
for x in range(0,len(BBand_sma),1):
BBand_mid.append((BBand_up[x]+BBand_down[x])/2)
for x in range(0,len(BBand_mid),1):
BBand_width.append(100*((BBand_up[x]-BBand_down[x])/BBand_mid[x]))
#print (BBand_width)
#print (max(BBand_width[20:]))
print ('----------')
#if(max(BBand_width[20:]))
#return data[:number_of_days]
max_bb=max(BBand_width[20:])
#print (max(BBand_width[20:]))
except Exception as e:
#print ('找不到所選資料,或發生異常錯誤.')
max_bb=0
print (max_bb)
return max_bb
像我想爬近三個月的成交量異常！？的股票，爬下來在分析再串自動下單！？，當然也要夠本，交易策略濾網要非常嚴謹，就可以放在家自動交易！？（後果不負責xd
多執行緒下次調１０個好了，這次不知道要被鎖幾天，被當成ddos囉
換連手機一下
快像了快像了
下次目標
---
假設經過一票海選後，像是成交量阿還是要搜尋什麼類型的技術指標阿等等等等，我們可以把爬蟲後的資料，下再下來或倒入資料庫，下次爬蟲的時候我們只要爬，最近一筆再把他家進資料庫，這樣就可以下次不必再依賴yahoo資料庫，直接對自己資料庫要資料，我們不只可以用tick來做高頻交易（還要串ａｐｉ交易，你相信中華電信的網路嗎xdd，等等打造一個看盤軟體，或向xq操盤大師那樣什麼策略警示，等等，至於資料量的多寡，恩恩自己去爬吧xd，接下來我們初步就完成一個最最最最基本的自動交易軟體的雛型囉，那麼ai真的真的真的要要來看一下囉，ㄎㄎ我會不會被一堆賣課程的針對‵，應該不會它們應該更有技術含量！
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