def DataDownloader():
start, end = [2010, 1, 1], [2019, 4, 1]
# keep the first row if duplicate company name
companies = pd.read_csv("companylist.csv").drop_duplicates(subset='Name',
keep="first")
companies = companies[companies.Sector == "Technology"]
Symbols = [symbol.rstrip() for symbol in companies.Symbol]
Stocks = Fetcher(
Symbols[0], start,
end).getHistorical().set_index("Date").rename_axis(None)[DATA]
Stocks.index = pd.DatetimeIndex(Stocks.index)
Stocks.columns = pd.MultiIndex.from_product([[Symbols[0]], Stocks.columns])
for s in Symbols[1:]:
df = Fetcher(
s, start,
end).getHistorical().set_index("Date").rename_axis(None)[DATA]
df.index = pd.DatetimeIndex(df.index)
df.columns = pd.MultiIndex.from_product([[s], df.columns])
if df.index[0] <= datetime.datetime(2010, 1, 4):
Stocks = pd.merge(
Stocks, df,
on=Stocks.index).set_index("key_0").rename_axis(None)
Stocks.to_csv(os.getcwd() + "/Stocks.csv")
def scrape_stock_price_f(stock_id, begin=begin_date, end=end_date):
# 抓取股票資料
stock_id = stock_id + ".TW"
df_temp = Fetcher(stock_id, begin, end).getHistorical()
#修正格式 ["dates","open","high","low","close","delta","volume"]
df_temp.iloc[:, 5] = df_temp.iloc[:, 4].rolling(2).apply(
lambda x: x.iloc[1] - x.iloc[0])
df_temp.columns = [
"dates", "open", "high", "low", "close", "delta", "volume"
]
'==========================增加欄位===================================='
#製作均線
df_temp["5MA"] = df_temp["close"].rolling(5).mean()
df_temp["10MA"] = df_temp["close"].rolling(10).mean()
df_temp["20MA"] = df_temp["close"].rolling(20).mean()
df_temp["60MA"] = df_temp["close"].rolling(60).mean()
#RSV
rsv =(df_temp['close']-df_temp['low'].rolling(window=9).min())/\
(df_temp['high'].rolling(window=9).max()-\
df_temp['low'].rolling(window=9).min())*100
df_temp["rsv"] = pd.DataFrame(rsv).fillna(0)
#製作KD線
df_temp["k"], df_temp["d"] = KD_line(df_temp)
#MACD
df_temp = pd.concat([df_temp, macd(df_temp)], axis=1)
#布林通道 (標準差的計算有問題)
df_temp = pd.concat([df_temp, bl_tunnel(df_temp)], axis=1)
# 增加股票代號
return df_temp
