def fixed_invest(self, code, data_list, pe_vec):
self._start_test(0, 10000)
all_days, curr_month = len(data_list[0]), -1
opens, highs, lows, closes = data_list[1], data_list[2], data_list[
3], data_list[4]
for _idx in range(0, all_days):
float_date = data_list[0][_idx]
int_date = StockDataSource.int_date(float_date)
self.print_progress(int_date)
stock_data = {
'ts_code': code,
'open': opens[_idx],
'high': highs[_idx],
'low': lows[_idx],
'close': closes[_idx]
}
_month = StockDataSource.datetime(int_date).month
self.account.ProfitDaily(int_date)
if curr_month != _month:
curr_month = _month
self.account.Rechange(10000)
kp = opens[_idx]
if pe_vec[_idx] > 20.83: # sell
_volume = self.account.credit / kp
elif pe_vec[_idx] > 13.89:
_volume = self.account.cash / kp
else:
_volume = self.account.cash / kp * 1.7
self._order(int_date, stock_data, _volume, kp, 0)
self.curr_closes[code] = closes[_idx]
self.account.UpdateValue(self.curr_closes, int_date)
self.market_values.append(self.account.total_value)
self.position_ratios.append(self.account.position_value /
self.account.total_value)
print(
pandas.DataFrame(self.hist_orders,
columns=[
'date', 'trade_price', 'stop_price', 'volume',
'total_value', 'credit', 'lever', 'ts_code'
]))
print(
pandas.DataFrame(self.year_values,
columns=['year', 'cash', 'ratio']))
self.account.status_info()
def daul_turtle0(self, code, data_list, index_long, index_short):
self._start_test(self.turtle_args[2])
init_max_count = 3
all_days, long_days, _max_count = len(data_list[0]), 0, init_max_count
long_state, short_state, max_value, trade_mode, high_price = 0, 0, 0, 0, 0
opens, highs, lows, closes = data_list[1], data_list[2], data_list[
3], data_list[4]
open_prices, open_dates, max_states = [], [], []
for _idx in range(self.turtle_args[2], all_days):
float_date = data_list[0][_idx]
int_date = StockDataSource.int_date(float_date)
self.print_progress(int_date)
if float_date < self.float_str or float_date > self.float_end:
# self.account.ProfitDaily()
self.market_values.append(self.account.total_value)
self.position_ratios.append(self.account.position_value /
self.account.total_value)
continue
# self.account.ProfitDaily(int_date)
self.curr_closes[code] = closes[_idx]
stock_data = {
'ts_code': code,
'key_price': index_long['key_price'][_idx],
'open': opens[_idx],
'high': highs[_idx],
'low': lows[_idx],
'close': closes[_idx]
}
if (not index_long['state'][_idx] or
self.account.total_value < max_value * .6) and trade_mode:
if self.account.total_value < max_value * .6:
print(int_date, self.account.total_value / max_value)
stock_data['key_price'] = closes[_idx]
# if not index_long['state'][_idx] and trade_mode:
if len(self.account.stocks):
self.single_clear(stock_data['key_price'], stock_data,
int_date)
max_value = self.account.total_value
if not index_long['state'][_idx]:
long_state, short_state, trade_mode = 0, 0, 0
self.order_count, _max_count = 0, init_max_count
max_states.append(index_long['state'][_idx - 1])
elif index_long['state'][_idx]:
long_days += 1
_wave = index_long['wave'][_idx]
while trade_mode in [0, 1] and long_state < index_long[
'state'][_idx] and long_state < _max_count:
# print( int_date, index_long['state'][_idx], index_short['state'][_idx], trade_mode, long_state, short_state )
long_state += 1
stock_data['key_price'] = index_long['key_price'][_idx] - (
index_long['state'][_idx] - long_state) * _wave
self.open_order(int_date, stock_data, _wave, opens[_idx])
if not trade_mode:
trade_mode = 1
# self.open_value = max(1000*10000, self.account.total_value)
max_value = self.open_value = self.account.total_value
open_prices.append(index_long['key_price'][_idx])
open_dates.append(float_date)
elif index_long['state'][_idx] >= 4 and trade_mode < 2:
trade_mode = 2
elif not index_short['state'][_idx-1] and index_short['state'][_idx] \
and index_long['state'][_idx] > 4 and self.order_count < 10:
short_state, trade_mode = 0, 4
if len(self.account.stocks):
_max_count = init_max_count
else:
_max_count = 1
# print( int_date, self.account.position_value, self.account.total_value )
if trade_mode == 2 and index_long['state'][_idx] >= 6 and len(open_prices) > 1 and \
(open_prices[-1] < open_prices[-2] or open_dates[-1] - open_dates[-2] > 300):
trade_mode, high_price = 3, highs[_idx]
# print( int_date, self.account.total_value / max_value, index_long['state'][_idx], index_short['state'][_idx] )
# print( int_date, self.order_count, long_state, index_long['state'][_idx], short_state, index_short['state'][_idx] )
if self.account.position_value > self.account.total_value * 7:
trade_mode = 5
while trade_mode in [4] and short_state < index_short['state'][
_idx] and short_state < _max_count:
short_state += 1
stock_data[
'key_price'] = index_short['key_price'][_idx] - (
index_short['state'][_idx] - short_state) * _wave
self.open_order(int_date, stock_data, _wave, opens[_idx])
if trade_mode == 3:
if high_price < highs[_idx]:
high_price = highs[_idx]
if lows[_idx] < high_price - _wave * 4:
self.single_clear(high_price - _wave * 4, stock_data,
int_date)
self.order_count = 0
self.account.UpdateValue(self.curr_closes, int_date)
max_value = max(max_value, self.account.total_value)
self.market_values.append(self.account.total_value)
self.position_ratios.append(self.account.position_value /
self.account.total_value)
order_list = pandas.DataFrame(self.hist_orders,
columns=[
'date', 'trade_price', 'stop_price',
'volume', 'total_value', 'credit',
'lever', 'ts_code'
])
out_list = order_list[[
'date', 'trade_price', 'volume', 'total_value', 'credit', 'lever'
]]
for i in range(0, len(out_list), 30):
print(out_list[i:i + 30])
print(
pandas.DataFrame(self.year_values,
columns=['year', 'cash', 'ratio']))
if len(open_dates) > len(max_states):
max_states.append(index_long['state'][-1])
open_dates = list(
map(lambda x: StockDataSource.str_date(x), open_dates))
print(
pandas.DataFrame({
"date": open_dates,
"price": open_prices,
"max_state": max_states
}))
print(
pandas.DataFrame(
self.records,
columns=['date', 'price', 'total', 'cash', 'profit', 'ratio']))
print('all day %d, long day %d.' % (all_days, long_days))
self.account.status_info()
def simple_daul_turtle(self, code, data_list, index_long, index_short):
self._start_test()
init_max_count = 3
all_days, long_days, _max_count = len(data_list[0]), 0, init_max_count
long_state, max_value, trade_mode = 0, 0, 0
opens, highs, lows, closes = data_list[1], data_list[2], data_list[
3], data_list[4]
open_prices, open_dates, max_states = [], [], []
for _idx in range(self.turtle_args[0], all_days):
float_date = data_list[0][_idx]
int_date = StockDataSource.int_date(float_date)
self.print_progress(int_date)
if float_date < self.float_str or float_date > self.float_end:
# self.account.ProfitDaily()
self.market_values.append(self.account.total_value)
self.position_ratios.append(self.account.position_value /
self.account.total_value)
continue
self.curr_closes[code] = closes[_idx]
self.account.ProfitDaily(int_date)
stock_data = {
'ts_code': code,
'key_price': index_short['key_price'][_idx],
'open': opens[_idx],
'high': highs[_idx],
'low': lows[_idx],
'close': closes[_idx]
}
if not index_short['state'][_idx] and trade_mode:
if len(self.account.stocks):
self.single_clear(stock_data, int_date)
long_state, trade_mode = 0, 0
self.order_count, _max_count = 0, init_max_count
max_states.append(index_long['state'][_idx - 1])
elif index_short['state'][_idx]:
long_days += 1
if not trade_mode:
trade_mode = 1
# self.open_value = max(1000*10000, self.account.total_value)
max_value = self.open_value = self.account.total_value
open_prices.append(index_long['key_price'][_idx])
open_dates.append(float_date)
if long_state < index_short['state'][
_idx] and long_state < _max_count:
self.open_order(int_date, stock_data,
index_short['wave'][_idx],
stock_data['key_price'], opens[_idx])
long_state = index_short['state'][_idx]
self.account.UpdateValue(self.curr_closes, int_date)
max_value = max(max_value, self.account.total_value)
self.market_values.append(self.account.total_value)
self.position_ratios.append(self.account.position_value /
self.account.total_value)
order_list = pandas.DataFrame(self.hist_orders,
columns=[
'date', 'trade_price', 'stop_price',
'volume', 'total_value', 'credit',
'lever', 'ts_code'
])
out_list = order_list[[
'date', 'trade_price', 'volume', 'total_value', 'credit', 'lever'
]]
for i in range(0, len(out_list), 30):
print(out_list[i:i + 30])
print(
pandas.DataFrame(self.year_values,
columns=['year', 'cash', 'ratio']))
if len(open_dates) > len(max_states):
max_states.append(index_long['state'][-1])
print(
pandas.DataFrame({
"date": open_dates,
"price": open_prices,
"max_state": max_states
}))
print(
pandas.DataFrame(
self.records,
columns=['date', 'price', 'total', 'cash', 'profit', 'ratio']))
print('all day %d, long day %d.' % (all_days, long_days))
self.account.status_info()
def single_turtle(self, code, data_list, index_long):
self._start_test(self.turtle_args[2])
self.open_value, _loss_unit, _open_unit = 0, 0, 0
dates, opens, highs, lows, closes = data_list[0], data_list[
1], data_list[2], data_list[3], data_list[4]
all_days, long_days, long_state = len(dates), 0, 0
for _idx in range(self.turtle_args[0], all_days):
float_date = dates[_idx]
int_date = StockDataSource.int_date(float_date)
self.print_progress(int_date)
if float_date < self.float_str or float_date > self.float_end:
# self.account.ProfitDaily()
self.market_values.append(self.account.total_value)
self.position_ratios.append(self.account.position_value /
self.account.total_value)
continue
self.curr_closes[code] = closes[_idx]
self.account.ProfitDaily(int_date)
stock_data = {
'ts_code': code,
'key_price': index_long['key_price'][_idx],
'open': opens[_idx],
'high': highs[_idx],
'low': lows[_idx],
'close': closes[_idx]
}
if not index_long['state'][_idx] and len(self.account.stocks):
self.single_clear(stock_data['key_price'], stock_data,
int_date)
long_state = 0
_long_open = long_state < index_long['state'][
_idx] and long_state < 2
if _long_open:
# and self.account.credit < self.account.total_value*2:
if not long_state:
self.open_value = self.account.total_value
key_price = index_long['key_price'][_idx]
if key_price < opens[_idx]:
key_price = opens[_idx]
_loss_unit = self.account.total_value * self.turtle_args[
6] * .001
_open_unit = _loss_unit / index_long['wave'][_idx]
_loss_price = key_price - index_long['wave'][
_idx] * self.turtle_args[0]
if self._order(int_date, stock_data, _open_unit, key_price,
_loss_price):
long_state = index_long['state'][_idx]
if index_long['state']:
long_days += 1
self.account.UpdateValue(self.curr_closes, int_date)
self.market_values.append(self.account.total_value)
self.position_ratios.append(self.account.position_value /
self.account.total_value)
print(pandas.DataFrame(self.year_values, columns=['year', 'cash']))
print(
pandas.DataFrame(
self.records,
columns=['date', 'price', 'total', 'cash', 'profit', 'ratio']))
# print( self.account.get_records()[[ 'order_time', 'price', 'volume', 'cash', 'credit' ]] )
print('all day %d, long day %d.' % (all_days, long_days))
self.account.status_info()
def daul_turtle1(self, code, data_list, index_long, index_short):
self._start_test(self.turtle_args[2])
self.open_value, _loss_unit, _open_unit = 0, 0, 0
opens, highs, lows, closes = data_list[1], data_list[2], data_list[
3], data_list[4]
all_days, long_days, long_state, short_state, trade_mode, order_count = len(
data_list[0]), 0, 0, 0, 0, 0
open_prices, open_dates, max_states = [], [], []
for _idx in range(self.turtle_args[0], all_days):
float_date = data_list[0][_idx]
int_date = StockDataSource.int_date(float_date)
self.print_progress(int_date)
if float_date < self.float_str or float_date > self.float_end:
# self.account.ProfitDaily()
self.market_values.append(self.account.total_value)
self.position_ratios.append(self.account.position_value /
self.account.total_value)
continue
self.curr_closes[code] = closes[_idx]
self.account.ProfitDaily(int_date)
stock_data = {
'ts_code': code,
'key_price': index_long['key_price'][_idx],
'open': opens[_idx],
'high': highs[_idx],
'low': lows[_idx],
'close': closes[_idx]
}
if not index_long['state'][_idx] and order_count:
if len(self.account.stocks):
self.single_clear(stock_data['key_price'], stock_data,
int_date)
long_state, short_state, trade_mode, order_count = 0, 0, 0, 0
max_states.append(index_long['state'][_idx - 1])
else:
long_days += 1
if len(self.account.stocks) and index_long['state'][_idx] >= 6 and not trade_mode and len(open_prices) > 1 and \
(open_prices[-1] < open_prices[-2] or open_dates[-1] - open_dates[-2] > 300):
self.single_clear(stock_data['key_price'], stock_data,
int_date)
if not index_short['state'][
_idx] and index_long['state'][_idx] > 4:
short_state, trade_mode = 0, 1
_long_on = long_state < index_long['state'][
_idx] and long_state < 2
_short_on = trade_mode and short_state < index_short['state'][
_idx] and short_state < 2
if (
_long_on or _short_on
) and order_count < 8: #and self.account.credit < self.account.total_value*2:
if not long_state: # or not short_state:
self.open_value = max(1000 * 10000,
self.account.total_value)
open_prices.append(index_long['key_price'][_idx])
open_dates.append(float_date)
print(int_date, order_count, long_state, short_state,
index_short['state'][_idx])
if _long_on:
key_price = index_long['key_price'][_idx]
elif _short_on:
key_price = index_short['key_price'][_idx]
if key_price < opens[_idx]:
key_price = opens[_idx]
_total_value = max(1000 * 10000, self.account.total_value)
_loss_unit = _total_value * self.turtle_args[6] * .001
_open_unit = _loss_unit / index_long['wave'][_idx]
_loss_price = key_price - index_long['wave'][
_idx] * self.turtle_args[0]
self._order(int_date, stock_data, _open_unit, key_price,
_loss_price)
order_count += 1
long_state = index_long['state'][_idx]
short_state = index_short['state'][_idx]
self.account.UpdateValue(self.curr_closes, int_date)
self.market_values.append(self.account.total_value)
self.position_ratios.append(self.account.position_value /
self.account.total_value)
order_list = pandas.DataFrame(self.hist_orders,
columns=[
'date', 'trade_price', 'stop_price',
'volume', 'total_value', 'credit',
'lever', 'ts_code'
])
out_list = order_list[[
'date', 'trade_price', 'volume', 'total_value', 'credit', 'lever'
]]
for i in range(0, len(out_list), 30):
print(out_list[i:i + 30])
print(
pandas.DataFrame(self.year_values,
columns=['year', 'cash', 'ratio']))
if len(open_dates) > len(max_states):
max_states.append(index_long['state'][-1])
print(open_dates)
print(max_states)
print(
pandas.DataFrame({
"date": open_dates,
"price": open_prices,
"max_state": max_states
}))
print(
pandas.DataFrame(
self.records,
columns=['date', 'price', 'total', 'cash', 'profit', 'ratio']))
print('all day %d, long day %d.' % (all_days, long_days))
self.account.status_info()
def fixed_invest(self, code, data_list, index_long):
self._start_test(0, 10 * 10000)
all_days, curr_month = len(data_list[0]), -1
opens, highs, lows, closes = data_list[1], data_list[2], data_list[
3], data_list[4]
for _idx in range(0, all_days):
float_date = data_list[0][_idx]
int_date = StockDataSource.int_date(float_date)
self.print_progress(int_date)
stock_data = {
'ts_code': code,
'open': opens[_idx],
'high': highs[_idx],
'low': lows[_idx],
'close': closes[_idx]
}
_month = StockDataSource.datetime(int_date).month
self.account.ProfitDaily(int_date)
if curr_month != _month:
curr_month = _month
self.account.Rechange(10000)
# _open_unit = self.account.cash / opens[_idx]
# _open_unit = (self.account.cash + 120000 - self.account.credit) / opens[_idx]
# self._order(int_date, stock_data, _open_unit, opens[_idx], 0)
# print( int_date, self.account.cash, self.account.credit )
kp = index_long['short'][_idx] * 1.05
kp = index_long['long'][_idx] * 0.95
_cash = self.account.cash + 120000 - self.account.credit
if lows[_idx] < kp and _cash > kp * 100:
if kp > opens[_idx]:
kp = opens[_idx]
# if highs[_idx] > kp and _cash > kp*100:
# if kp < opens[_idx]:
# kp = opens[_idx]
_open_unit = _cash / kp
self._order(int_date, stock_data, _open_unit, kp, 0)
self.curr_closes[code] = closes[_idx]
self.account.UpdateValue(self.curr_closes, int_date)
self.market_values.append(self.account.total_value)
self.position_ratios.append(self.account.position_value /
self.account.total_value)
print(
pandas.DataFrame(self.hist_orders,
columns=[
'date', 'trade_price', 'stop_price', 'volume',
'total_value', 'credit', 'lever', 'ts_code'
]))
print(
pandas.DataFrame(self.year_values,
columns=['year', 'cash', 'ratio']))
self.account.status_info()
