def active_strategy(start_date,
end_date,
update=False,
source="cp68",
market="^VNINDEX"):
symbols = getliststocks(typestock=market)
for ticker in symbols:
try:
# ninja_trading(ticker, start, end, realtime = update, source = source)
# hedgefund_trading(ticker, start, end, realtime = update, source = source)
hung_canslim(ticker,
start=start_date,
end=end_date,
realtime=update,
source=source,
market=market,
ndays=2,
typetrade='Long')
# mean_reversion(ticker, start, end, realtime = update, source = source)
# bollinger_bands(ticker, start, end, realtime = update, source = source)
# short_selling(ticker, start, end, realtime = update, source = source)
except Exception as e:
print(e)
print("Error in reading symbol: ", ticker)
pass
def analysis_trading(tickers,
start,
end,
update=False,
source="cp68",
trade='Long'):
if tickers == None:
tickers = getliststocks(typestock="TICKER")
if tickers == 'VN30':
tickers = getliststocks(typestock="VN30")
# data = pd.read_csv('fundemental_stocks_all.csv', parse_dates=True, index_col=0)
# data['Diff_Price'] = data['Close'] - data['EPS']*data['PE']/1000
# data['EPS_Price'] = data['EPS']/data['Close']/1000
# df = data.query("MeanVol_10W > 80000")
# df = data.query("MeanVol_13W > 80000")
# df = df.query("EPS > 1000")
# df = df.query("ROE > 15")
#
# canslim_symbol = df.index.tolist()
#
# tickers = canslim_symbol
for ticker in tickers:
# print(" Analysing ..." , ticker)
try:
# ninja_trading(ticker, start, end, realtime = update, source = source)
# hedgefund_trading(ticker, start, end, realtime = update, source = source)
# hung_canslim(ticker, start, end, realtime = update, source = source, ndays = 5, typetrade = 'MarkM_tickers')#
hung_canslim(ticker,
start,
end,
realtime=update,
source=source,
ndays=2,
typetrade=trade)
# hung_canslim(ticker, start, end, realtime = update, source = source, ndays = 3, typetrade = 'Short')
# mean_reversion(ticker, start, end, realtime = update, source = source)
# bollinger_bands(ticker, start, end, realtime = update, source = source)
# short_selling(ticker, start, end, realtime = update, source = source, ndays = 2, typetrade = 'Short')
except Exception as e:
print(e)
print("Error in reading symbol: ", ticker)
pass
def analysis_trading(tickers,
start,
end,
update=False,
nbdays=15,
source="cp68",
trade='Long'):
if tickers == None:
tickers = getliststocks(typestock="TICKER")
if tickers == 'VN30':
tickers = getliststocks(typestock="VN30")
# data = pd.read_csv('fundemental_stocks_all.csv', parse_dates=True, index_col=0)
# data['Diff_Price'] = data['Close'] - data['EPS']*data['PE']/1000
# data['EPS_Price'] = data['EPS']/data['Close']/1000
# df = data.query("MeanVol_10W > 80000")
# df = data.query("MeanVol_13W > 80000")
# df = df.query("EPS > 1000")
# df = df.query("ROE > 15")
#
# canslim_symbol = df.index.tolist()
#
# tickers = canslim_symbol
# result = pd.DataFrame([['Ticker', 'Advise']])
result = pd.DataFrame(columns=['Ticker', 'Advise', 'PCT', 'Close'])
result = result.set_index('Ticker')
for ticker in tickers:
# print(" Analysing ..." , ticker)
try:
# ninja_trading(ticker, start, end, realtime = update, source = source)
# hedgefund_trading(ticker, start, end, realtime = update, source = source)
# hung_canslim(ticker, start, end, realtime = update, source = source, ndays = 5, typetrade = 'MarkM_tickers')#
res = hung_canslim(ticker,
start,
end,
realtime=update,
source=source,
ndays=nbdays,
typetrade=trade)
if len(res) > 1:
# result = result.append([res])
result.loc[res[0]] = [res[1], 100 * res[2], res[3]]
# hung_canslim(ticker, start, end, realtime = update, source = source, ndays = 3, typetrade = 'Short')
# mean_reversion(ticker, start, end, realtime = update, source = source)
# bollinger_bands(ticker, start, end, realtime = update, source = source)
# short_selling(ticker, start, end, realtime = update, source = source, ndays = 2, typetrade = 'Short')
except Exception as e:
print(e)
print("Error in reading symbol: ", ticker)
pass
return result