def testPolicy_Qstick(self, symbol, sd, ed, sv):
df_trades = pd.DataFrame({'Date':'','trade':''}, index=[])
options = [1000, -1000, 0]
symbols = []
symbols.append(symbol)
data = util.get_data(symbols = symbols, dates= pd.date_range(sd,ed))
data = pd.DataFrame(data[symbol])
data_pct = data.pct_change()
price_indicator, qstick = indicator.main(symbol, sd, ed)
price_indicator['up_sale'] = price_indicator['upper']/price_indicator['price']
price_indicator['down_buy'] = price_indicator['lower']/price_indicator['price']
length = len(data)
threshold = 0.8
for i in range(20,length):
sale_indicator = price_indicator['up_sale'].iloc[i]
buy_indicator = price_indicator['down_buy'].iloc[i]
indicator_qstick = qstick['Qstick'] .iloc[i]
# change = ratio.iloc[i][0]
date = data.iloc[i].name
# date = date_lst[i]
holding = df_trades.trade.sum()
if holding == 0:
if indicator_qstick < 0:
trade = options[0]
df_trades = df_trades.append({'Date':date, 'trade': trade}, ignore_index=True)
elif indicator_qstick > 0:
trade = options[1]
df_trades = df_trades.append({'Date':date, 'trade': trade}, ignore_index=True)
else:
continue
else:
# if indicator_volume > 1 and holding < 0:
if buy_indicator > threshold and holding <0:
trade = options[0] + 1000
df_trades = df_trades.append({'Date': date, 'trade': trade}, ignore_index=True)
elif sale_indicator < threshold and holding > 0:
trade = options[1] - 1000
df_trades = df_trades.append({'Date': date, 'trade': trade}, ignore_index=True)
elif indicator_qstick < 0 and holding < 0:
# buy 1000 shares, long 1000 shares
trade = options[0] + 1000
df_trades = df_trades.append({'Date': date, 'trade': trade}, ignore_index=True)
elif indicator_qstick < 0 and holding < 0 :
# buy 1000 shares, long 1000 shares
trade = options[0] + 1000
df_trades = df_trades.append({'Date':date, 'trade': trade}, ignore_index=True)
elif indicator_qstick > 0 and holding >0:
# sell 1000 shares, short 1000 shares
trade = options[1] - 1000
df_trades = df_trades.append({'Date':date, 'trade': trade}, ignore_index=True)
else:
continue
return df_trades
def benchmark(code, comparator): """Compares indicators of similar cities. Benchmark can be maid on either population, budget or location similarity """ if comparator=="pop": cities = similar_pop(code) elif comparator=="budget": cities = similar_budget(code) elif comparator=="location": cities = similar_loc(code) tab = [indicator.main(code)[0]] for city in cities: col_city = indicator.main(city) tab.append(col_city[1]) return(tab)
def process(function, argument):
code = get_code(argument)
if code == None:
raise InputError
if function == 'indicator':
print(indicator.main(code))
elif function == 'benchmark':
print(benchmark.main(code))
def testPolicy_only_bands(self, symbol, sd, ed, sv):
df_trades = pd.DataFrame({'Date':'','trade':''}, index=[])
options = [1000, -1000, 0]
symbols = []
symbols.append(symbol)
data = util.get_data(symbols = symbols, dates= pd.date_range(sd,ed))
data = pd.DataFrame(data['JPM'])
data_pct = data.pct_change()
price_indicator, what = indicator.main(symbol, sd, ed)
price_indicator['up_sale'] = price_indicator['upper']/price_indicator['price']
price_indicator['down_buy'] = price_indicator['lower']/price_indicator['price']
length = len(data)
threshold = 1
for i in range(20,length):
sale_indicator = price_indicator['up_sale'].iloc[i]
buy_indicator = price_indicator['down_buy'].iloc[i]
date = data.iloc[i].name
holding = df_trades.trade.sum()
if holding == 0:
if buy_indicator > threshold:
trade = options[0]
df_trades = df_trades.append({'Date':date, 'trade': trade}, ignore_index=True)
elif sale_indicator < threshold:
trade = options[1]
df_trades = df_trades.append({'Date':date, 'trade': trade}, ignore_index=True)
else:
continue
else:
if buy_indicator > threshold and holding < 0 :
# buy 1000 shares, long 1000 shares
trade = options[0] + 1000
df_trades = df_trades.append({'Date':date, 'trade': trade}, ignore_index=True)
elif sale_indicator < threshold and holding >0:
# sell 1000 shares, short 1000 shares
trade = options[1] - 1000
df_trades = df_trades.append({'Date':date, 'trade': trade}, ignore_index=True)
else:
continue
# continue
return df_trades
def testPolicy_nvi(self, symbol, sd, ed, sv):
df_trades = pd.DataFrame({'Date':'','trade':''}, index=[])
options = [1000, -1000, 0]
symbols = []
symbols.append(symbol)
data = util.get_data(symbols = symbols, dates= pd.date_range(sd,ed))
data = pd.DataFrame(data['JPM'])
data_pct = data.pct_change()
price_indicator, volume_indicator = indicator.main(symbol, sd, ed)
price_indicator['up_sale'] = price_indicator['upper']/price_indicator['price']
price_indicator['down_buy'] = price_indicator['lower']/price_indicator['price']
volume_indicator['volume_indicator_pvi'] = volume_indicator['PVI'] / volume_indicator['es_PVI']
volume_indicator['volume_indicator_nvi'] = volume_indicator['NVI'] / volume_indicator['es_NVI']
# SMA_change = SMA.pct_change().shift(-5)
# print price_indicator.head()
# date_lst = data.index
length = len(data)
buy_threshold = 1
sell_threshold = 1
# depends on individual risk preference.
# when buy_threshold = 0.6
# sell_threshold = 0.3
# performance is pretty good
# the threshold depends on the volality
for i in range(0,length):
# print price_indicator['up_sale'].iloc[i]
# sale_indicator = price_indicator['up_sale'].iloc[i]
# buy_indicator = price_indicator['down_buy'].iloc[i]
indicator_volume = volume_indicator['volume_indicator_pvi'] .iloc[i]
# change = ratio.iloc[i][0]
date = data.iloc[i].name
# date = date_lst[i]
holding = df_trades.trade.sum()
if holding == 0:
if indicator_volume <1:
trade = options[0]
df_trades = df_trades.append({'Date':date, 'trade': trade}, ignore_index=True)
# elif sale_indicator > sell_threshold:
# trade = options[1]
# df_trades = df_trades.append({'Date':date, 'trade': trade}, ignore_index=True)
else:
trade = options[1]
df_trades = df_trades.append({'Date':date, 'trade': trade}, ignore_index=True)
# continue
else:
if indicator_volume <1 and holding < 0:
# if buy_indicator > buy_threshold and holding < 0 :
# buy 1000 shares, long 1000 shares
trade = options[0] + 1000
df_trades = df_trades.append({'Date':date, 'trade': trade}, ignore_index=True)
# elif sale_indicator > sell_threshold and holding >0:
# # sell 1000 shares, short 1000 shares
# trade = options[1] - 1000
# df_trades = df_trades.append({'Date':date, 'trade': trade}, ignore_index=True)
elif indicator_volume >1 and holding > 0:
# continue
trade = options[1] - 1000
df_trades = df_trades.append({'Date': date, 'trade': trade}, ignore_index=True)
else:
continue
# continue
return df_trades