def get_window_profit(code, start_date, end_date):
initial_deposit = 10000000
initial_price = 0
money = initial_deposit
prev_close = 0
bought = {'quantity': 0, 'price': 0, 'balance': 0}
trade_count = 0
today = start_date
while today < end_date:
while today.weekday() > 4:
today += timedelta(days=1)
_, today_data = stock_chart.get_day_period_data(code, today, today)
price_list = list(
map(lambda x: {
'high': x['3'],
'low': x['4'],
'close': x['5']
}, today_data))
if len(price_list) == 0:
print('No data')
today += timedelta(days=1)
continue
_, data = stock_chart.get_day_period_data(code,
today - timedelta(days=40),
today - timedelta(days=1))
low = price_list[0]['low']
high = price_list[0]['high']
close = price_list[0]['close']
mac = MovingAverageCross(data, 20, 120)
mac.generate_signals()
last_position = mac.df.signal[-1]
if bought['quantity'] != 0 and last_position == 0:
money = close * bought['quantity']
money -= money * 0.003
print('SELL', close)
bought['quantity'] = 0
elif bought['quantity'] == 0 and last_position == 1 and prev_close != 0:
bought['quantity'] = money / close
money = 0
trade_count += 1
print('BUY', close)
if initial_price == 0:
initial_price = close
prev_close = close
left = money if money != 0 else bought['quantity'] * prev_close
yield today, left / initial_deposit * 100, last_position, close / initial_price * 100, trade_count
print(today, 'P:', '{0:0.3f}'.format(left / initial_deposit * 100),
'POS:', last_position,
'{0:0.3f}'.format(close / initial_price * 100))
today += timedelta(days=1)
def get_profit(code, start_date, end_date, buy_rate, sell_rate):
initial_deposit = 10000000
money = initial_deposit
prev_close = 0
bought = {'quantity': 0, 'price': 0}
trade_count = 0
while end_date > start_date:
start_date += timedelta(days=1)
l, data = stock_chart.get_day_period_data(
code, start_date, start_date + timedelta(days=1))
if l == 0: continue
high, low, close = data[0]['3'], data[0]['4'], data[0]['5']
buy_threshold = prev_close * buy_rate
sell_threshold = prev_close * sell_rate
if bought['quantity'] is not 0 and high >= prev_close + buy_threshold:
money = close * bought['quantity']
money -= money * 0.003
bought['quantity'] = 0
elif bought[
'quantity'] is 0 and prev_close != 0 and low <= prev_close - sell_threshold:
bought['quantity'] = money / close
money = 0
trade_count += 1
prev_close = close
left = money if money is not 0 else bought['quantity'] * prev_close
return (left / initial_deposit * 100, trade_count)
def get_window_profit(code, start_date, end_date, method):
initial_deposit = 10000000
money = initial_deposit
prev_close = 0
bought = {'quantity': 0, 'price': 0, 'balance': 0}
trade_count = 0
method_apply = {
profit_calc.NORMAL: profit_calc.right_profit,
profit_calc.SHORT: profit_calc.short_profit,
profit_calc.MEET_DESIRED_PROFIT: profit_calc.right_sell_profit,
profit_calc.BUY_WHEN_BEARISH: profit_calc.left_profit,
profit_calc.LIMIT: profit_calc.right_limit_profit,
}
today = start_date
while today < end_date:
while today.weekday() > 4:
today += timedelta(days=1)
s = speculation.Speculation()
sdf = s.get_speculation(today, [code], method)
if len(sdf) == 0:
today += timedelta(days=1)
continue
buy_rate = sdf.iloc[0]['buy_rate']
sell_rate = sdf.iloc[0]['sell_rate']
_, data = stock_chart.get_day_period_data(code, today, today)
price_list = list(map(lambda x: {'high': x['3'], 'low': x['4'], 'close': x['5']}, data))
if len(price_list) == 0:
print('No data')
today += timedelta(days=1)
continue
elif len(price_list) > 1:
print('Something wrong', today)
low = price_list[0]['low']
high = price_list[0]['high']
close = price_list[0]['close']
buy_threshold = prev_close * buy_rate
sell_threshold = prev_close * sell_rate
money, t = method_apply[method](bought, low, high, close,
prev_close, buy_threshold, sell_threshold, money, trade_count)
trade_count += t
prev_close = close
today += timedelta(days=1)
if method == profit_calc.SHORT:
left = money if money is not 0 else (bought['price'] - prev_close) * bought['quantity'] + bought['balance']
else:
left = money if money != 0 else bought['quantity'] * prev_close
return left / initial_deposit * 100, trade_count
def get_window_profit(code, start_date, end_date):
initial_deposit = 10000000
initial_price = 0
money = initial_deposit
prev_close = 0
bought = {'quantity': 0, 'price': 0, 'balance': 0}
trade_count = 0
today = start_date
while today < end_date:
while today.weekday() > 4:
today += timedelta(days=1)
s = speculation.Speculation()
sdf = s.get_speculation(today, [code], profit_calc.MEET_DESIRED_PROFIT)
if len(sdf) == 0:
today += timedelta(days=1)
continue
buy_rate = sdf.iloc[0]['buy_rate']
sell_rate = sdf.iloc[0]['sell_rate']
_, data = stock_chart.get_day_period_data(code, today, today)
price_list = list(map(lambda x: {'high': x['3'], 'low': x['4'], 'close': x['5']}, data))
if len(price_list) == 0:
print('No data')
today += timedelta(days=1)
continue
elif len(price_list) > 1:
print('Something wrong', today)
low = price_list[0]['low']
high = price_list[0]['high']
close = price_list[0]['close']
sell_threshold = prev_close * sell_rate
buy_threshold = prev_close * buy_rate
#print('CLOSE:', close, 'LOW:', low, 'SELL_THRESHOLD')
if bought['quantity'] != 0 and (low <= prev_close - sell_threshold or sdf.iloc[0]['profit_expected'] < 100.):
money = close * bought['quantity']
money -= money * 0.003
bought['quantity'] = 0
elif bought['quantity'] == 0 and prev_close != 0 and sdf.iloc[0]['profit_expected'] > 100. and high >= prev_close + buy_threshold:
bought['quantity'] = money / close
money = 0
trade_count += 1
if initial_price == 0:
initial_price = close
prev_close = close
left = money if money != 0 else bought['quantity'] * prev_close
yield today, left / initial_deposit * 100, sdf.iloc[0]['profit_expected'], sell_rate, close / initial_price * 100, trade_count
print(today, 'P:', '{0:0.3f}'.format(left / initial_deposit * 100), 'E:', '{0:0.3f}'.format(sdf.iloc[0]['profit_expected']), '{0:0.3f}'.format(sell_rate), '{0:0.3f}'.format(close / initial_price * 100))
today += timedelta(days=1)
def get_yesterday(yesterday):
max_depth = 10
while True:
if max_depth <= 0:
return None, None
l, data = stock_chart.get_day_period_data(code, yesterday, yesterday + timedelta(days=1))
if l == 0:
yesterday = yesterday - timedelta(days=1)
max_depth -= 1
else: break
return yesterday - timedelta(days=1), data
def get_tomorrow(tomorrow):
max_depth = 10
while True:
if max_depth <= 0:
return None, None
l, data = stock_chart.get_day_period_data(code, tomorrow,
tomorrow + timedelta(days=1))
if l == 0:
tomorrow = tomorrow + timedelta(days=1)
max_depth -= 1
else:
break
return tomorrow + timedelta(days=1), data
self.df['signal'] = 0.0
self.df['mavg'] = self.df['close'].rolling(self.period,
min_periods=1).mean()
self.df['emavg'] = self.df['close'].ewm(span=self.period,
min_periods=1,
adjust=False,
ignore_na=False).mean()
self.df['signal'][self.period:] = np.where(
self.df.emavg[self.period:] < self.df['close'][self.period:], 1.0,
0.0)
self.df['positions'] = self.df['signal'].diff()
print(self.df)
if __name__ == '__main__':
_, data = stock_chart.get_day_period_data('A005380', datetime(2015, 1, 1),
datetime(2018, 11, 30))
mac = MovingAverageCross(data, 20)
mac.generate_signals()
fig = plt.figure()
fig.patch.set_facecolor('white')
ax1 = fig.add_subplot(111, ylabel='price')
mac.df['close'].plot(ax=ax1, color='r', lw=2.)
mac.df[['mavg', 'emavg']].plot(ax=ax1, lw=2.)
ax1.plot(mac.df.ix[mac.df.positions == -1.0].index,
mac.df.close[mac.df.positions == -1.0],
'v',
markersize=10,
color='k')
ax1.plot(mac.df.ix[mac.df.positions == 1.0].index,
def get_window_profit(code, start_date, end_date, cons):
initial_deposit = 10000000
initial_price = 0
money = initial_deposit
prev_close = 0
bought = {'quantity': 0, 'price': 0, 'balance': 0}
trade_count = 0
today = start_date
while today < end_date:
while today.weekday() > 4:
today += timedelta(days=1)
_, data = stock_chart.get_day_period_data(code, today, today)
price_list = list(map(lambda x: {'high': x['3'], 'low': x['4'], 'close': x['5']}, data))
if len(price_list) == 0:
print('No data')
today += timedelta(days=1)
continue
elif len(price_list) > 1:
print('Something wrong', today)
high_past = []
low_past = []
collecting = False
yesterday = today - timedelta(days=1)
while True:
if len(high_past) == cons:
collecting = True
break
yesterday, ydata = get_yesterday(yesterday)
if ydata is None:
break
ylist = list(map(lambda x: {'high': x['3'], 'low': x['4'], 'close': x['5']}, ydata))[0]
high_past.append(ylist['high'])
low_past.append(ylist['low'])
if not collecting:
print('failed to get past data')
continue
low = price_list[0]['low']
high = price_list[0]['high']
close = price_list[0]['close']
b_condition = max(high_past) < high
s_condition = min(low_past) > low
if bought['quantity'] != 0 and s_condition:
money = close * bought['quantity']
money -= money * 0.003
bought['quantity'] = 0
elif bought['quantity'] == 0 and b_condition and prev_close != 0:
bought['quantity'] = money / close
money = 0
trade_count += 1
if initial_price == 0:
initial_price = close
prev_close = close
left = money if money != 0 else bought['quantity'] * prev_close
yield today, left / initial_deposit * 100, close / initial_price * 100, trade_count
print(today, 'P:', '{0:0.3f}'.format(left / initial_deposit * 100), '{0:0.3f}'.format(close / initial_price * 100), 'T:', trade_count)
today += timedelta(days=1)
return today_low, today_max, today_close
print(today, '\t{0:0.2f}'.format(today_max),
'\t{0:0.2f}'.format(today_low), '\t{0:0.2f}'.format(today_close))
return None
#print(today)
if __name__ == '__main__':
code_list = stock_code.get_kospi200_list()
df = pd.DataFrame(columns=['code', 'date', 'low', 'max', 'close'])
for code in code_list:
today = datetime(2015, 1, 1)
test = 10
while datetime.now() > today:
l, data = stock_chart.get_day_period_data(
code, today, today + timedelta(days=1))
if l == 0:
today += timedelta(days=1)
continue
else:
r = get_today_profit(code, today, data)
if r is not None:
df = df.append(
{
'code': code,
'date': today,
'low': r[0],
'max': r[1],
'close': r[2]
},
ignore_index=True)
code_list = stock_code.get_kospi200_list()
start_date = datetime(2018, 10, 22)
s = speculation.Speculation()
sp_list = s.get_speculation(start_date, code_list)
df = pd.DataFrame(sp_list)
n = datetime.now()
df['real_profit'] = np.zeros(len(df))
for code in code_list:
_, data = stock_chart.get_day_period_data(code,
start_date + timedelta(days=1),
datetime.now())
price_list = list(
map(lambda x: {
'high': x['3'],
'low': x['4'],
'close': x['5']
}, data))
buy_rate = df[df['code'] == code].iloc[0]['buy_rate']
sell_rate = df[df['code'] == code].iloc[0]['sell_rate']
profit, _ = profit_calc.get_avg_profit_by_day_data(
pd.DataFrame(price_list), buy_rate, sell_rate)
df.loc[df['code'] == code, 'real_profit'] = profit
#code_list = ['A298040']
total_p = pd.DataFrame(columns=['code', 'profit'])
PERIOD = 24 * 3
for code in code_list:
df = pd.DataFrame(columns=[
'code', 'date', 'price', 'trend_upper', 'trend_lower', 'signal',
'bought', 'sold', 'volume', 'foreign', 'profit'
])
df.set_index('date')
bought_price = 0
total_profit = 100
count, total = stock_chart.get_day_period_data(code,
datetime(2016, 1, 1),
datetime(2018, 1, 1))
if count == 0:
print("NO DATA", code)
continue
day_window = PERIOD + 1
for i in range(0, count - day_window):
data = total[i:day_window + i]
ptl = PriceTrendLine(data)
result = ptl.generate_line()
bought, sold = False, False
profit = 0
skip_current = False
trend_upper, trend_lower = result[0][2], result[1][2]
code_list = ['A003850', 'A047050', 'A001740']
for loss in [-2]:
total_p = pd.DataFrame(columns=['code', 'profit'])
for code in code_list:
df = pd.DataFrame(columns=[
'code', 'date', 'price', 'trend_upper', 'trend_lower',
'buy_price', 'sell_price', 'profit'
])
today = datetime(2015, 1, 1)
bought_price = 0
total_profit = 100
skip_flow = False
count, total = stock_chart.get_day_period_data(
code, today, datetime.now())
day_window = 91 - 24 # 3 month + 1 day
for i in range(0, count - day_window):
data = total[i:day_window + i]
ptl = PriceTrendLine(data)
result = ptl.generate_line()
sell_price = 0
profit = 0
trend_upper, trend_lower = result[0][2], result[1][2]
if not (trend_upper and trend_lower):
skip_flow = False
if bought_price > 0 and not (trend_upper and trend_lower):
def generate_line(self):
upper_line = self._find_line(True)
lower_line = self._find_line(False)
return upper_line, lower_line
if __name__ == '__main__':
# A005930: 2016,10,6
if len(sys.argv) < 5:
print('python trendline.py [code] [year] [month] [day]')
sys.exit(1)
today = datetime(int(sys.argv[2]), int(sys.argv[3]), int(sys.argv[4]))
_, data = stock_chart.get_day_period_data(sys.argv[1],
today - timedelta(days=30),
today)
mac = PriceTrendLine(data)
upper_line, lower_line = mac.generate_line()
fig = plt.figure()
fig.patch.set_facecolor('white')
ax1 = fig.add_subplot(311, ylabel='price')
ax2 = fig.add_subplot(312, ylabel='volume')
ax3 = fig.add_subplot(313, ylabel='foreign')
mac.df['close'].plot(ax=ax1, color='r', lw=2.)
ax1.plot(upper_line[0], upper_line[1], color='b', lw=2., marker='o')
ax1.plot(lower_line[0], lower_line[1], color='g', lw=2., marker='o')
mac.df['volume'].plot(ax=ax2, color='g')
mac.df['foreign'].plot(ax=ax3, color='b')
plt.show()
self.calculating = df[:90]
self.simulation = {}
self.simulation['Actual'] = list(df.close.iloc[90:].values)
for i in range(RandomWalk.SIMULATION_COUNT):
self.simulation['Simulation' + str(i)] = [df.close.iloc[90]]
def generate_signals(self):
mu = self.calculating.close.pct_change().mean()
sigma = self.calculating.close.pct_change().std()
for s in range(RandomWalk.SIMULATION_COUNT):
for i in range(len(self.simulation['Actual']) - 1):
next_day = self.simulation['Simulation' + str(s)][-1] * np.exp((mu - (sigma ** 2 / 2)) + sigma * np.random.normal())
self.simulation['Simulation' + str(s)].append(next_day)
return self.simulation
if __name__ == '__main__':
_, data = stock_chart.get_day_period_data('A005380', datetime(2019, 1, 1), datetime.now())
mac = RandomWalk(data)
simulation_df = pd.DataFrame(mac.generate_signals())
fig = plt.figure()
fig.patch.set_facecolor('white')
ax1 = fig.add_subplot(111, ylabel='price')
_ = simulation_df.plot(ax=ax1)
#simulation_df[['Actual', 'Simulation']].plot(ax=ax1, lw=1.)
plt.show()
