def price_notification():
# TODO 監視対象企業が30社の場合に描画している図が多いという警告が出たのでなおす
# RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`
# ) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
# fig = plt.figure()
for brand in BrandRepo().select_all_brand():
code = brand.code
name = brand.name
file_path = f'temp/{code}_candle.png'
prices = BrandRepo().select_prices(code)
data = {
'date': list(map(lambda p: p.date, prices)),
'open': list(map(lambda p: p.open, prices)),
'high': list(map(lambda p: p.high, prices)),
'low': list(map(lambda p: p.low, prices)),
'close': list(map(lambda p: p.close, prices))
}
df = pd.DataFrame(data).set_index("date")
fig = plt.figure()
ax = plt.subplot()
xdate = [x.date() for x in df.index] # Timestamp -> datetime
ohlc = np.vstack((date2num(xdate), df.values.T)).T # datetime -> float
candlestick_ohlc(ax, ohlc, width=0.7, colorup='g', colordown='r')
ax.grid() # グリッド表示
register_matplotlib_converters()
ax.set_xlim(df.index[0].date(), df.index[-1].date()) # x軸の範囲
fig.autofmt_xdate() # x軸のオートフォーマット
plt.savefig(file_path)
slack_comment(f'{code} {name} ローソク足チャート')
time.sleep(10)
slack_file_upload(file_path)
time.sleep(10)
os.remove(file_path)
def save_stock():
def crawl(code):
time.sleep(30)
return map(lambda l: Price(code, l[0].to_pydatetime(), l[1], l[2], l[3], l[4], l[5], l[6]),
crawl_stock(code).values.tolist())
print("save stock start")
# TODO 一旦全レコード分メモリ展開するのは直す
BrandRepo().insert_stocks(
reduce(add, list(map(lambda brand: list(crawl(brand.code)), list(BrandRepo().select_all_brand())))))
print("save stock end")
def make_price_df(brands):
df = pd.DataFrame(columns=[
'code', 'date', 'open', 'high', 'low', 'close', 'golden_cross',
'dead_cross'
]).set_index(['code', 'date'])
for brand in brands:
code = brand.code
prices = BrandRepo().select_prices(code)
data = {
'code': list(map(lambda p: p.code, prices)),
'date': list(map(lambda p: p.date, prices)),
'open': list(map(lambda p: p.open, prices)),
'high': list(map(lambda p: p.high, prices)),
'low': list(map(lambda p: p.low, prices)),
'close': list(map(lambda p: p.close, prices))
}
brand_df = pd.DataFrame(data).set_index(['code', 'date'])
sma_5 = brand_df.groupby(level='code')['close'].rolling(
window=5).mean()
sma_25 = brand_df.groupby(level='code')['close'].rolling(
window=25).mean()
sma_5_over_25 = sma_5 > sma_25
cross = sma_5_over_25 != sma_5_over_25.shift(1)
golden_cross = cross & (sma_5_over_25 == True)
dead_cross = cross & (sma_5_over_25 == False)
brand_df['sma_5'] = brand_df.groupby(
level='code')['close'].rolling(window=5).mean().values
brand_df['sma_25'] = brand_df.groupby(
level='code')['close'].rolling(window=25).mean().values
brand_df['sma_5_over_25'] = sma_5_over_25.values
brand_df['golden_cross'] = golden_cross.values
brand_df['dead_cross'] = dead_cross.values
df = df.append(brand_df[25:], sort=True)
return df
def new_brand_notification():
df = pd.DataFrame(list(
map(
lambda new_brand:
(new_brand.code, new_brand.format_date(), new_brand.name),
BrandRepo().select_new_brand_recently())),
columns=["code", "date", "name"])
slack_comment("新株情報\n" + str(df))
def brand_notification():
df = pd.DataFrame(
list(
map(
lambda brand: (brand.code, brand.name, brand.short_name, brand.
market, brand.sector, brand.unit),
BrandRepo().select_all_brand())),
columns=["code", "name", "short_name", "market", "sector", "unit"])
slack_comment("監視対象株\n" + str(df))
def sync_brand():
def read_brand_codes(filepath):
with open(filepath) as f:
return filter(lambda code: code != '', map(lambda code: code.strip(), f.read().split("\n")))
def crawl_brands(new_codes):
for new_code in new_codes:
brand = get_brand(new_code)
print(brand)
yield Brand(brand[0], brand[1], brand[2], brand[3], brand[5], brand[4])
time.sleep(30)
print("sync brand start")
target_codes_set = set(read_brand_codes("brand_list.txt"))
saved_codes_set = set(map(lambda brand: brand.code, BrandRepo().select_all_brand()))
new_codes = filter(lambda target_code: target_code not in saved_codes_set, target_codes_set)
del_codes = filter(lambda saved_code: saved_code not in target_codes_set, saved_codes_set)
BrandRepo().insert_brands(list(crawl_brands(new_codes)))
BrandRepo().delete_brand_by_codes(list(del_codes))
print("sync brand end")
def save_newbrand():
BrandRepo().insert_new_brands(list(map(
lambda new_brand: NewBrand(new_brand[0], new_brand[1].strftime('%Y/%m/%d'), new_brand[2]),
get_new_brands2())))
def simulate():
def make_price_df(brands):
df = pd.DataFrame(columns=[
'code', 'date', 'open', 'high', 'low', 'close', 'golden_cross',
'dead_cross'
]).set_index(['code', 'date'])
for brand in brands:
code = brand.code
prices = BrandRepo().select_prices(code)
data = {
'code': list(map(lambda p: p.code, prices)),
'date': list(map(lambda p: p.date, prices)),
'open': list(map(lambda p: p.open, prices)),
'high': list(map(lambda p: p.high, prices)),
'low': list(map(lambda p: p.low, prices)),
'close': list(map(lambda p: p.close, prices))
}
brand_df = pd.DataFrame(data).set_index(['code', 'date'])
sma_5 = brand_df.groupby(level='code')['close'].rolling(
window=5).mean()
sma_25 = brand_df.groupby(level='code')['close'].rolling(
window=25).mean()
sma_5_over_25 = sma_5 > sma_25
cross = sma_5_over_25 != sma_5_over_25.shift(1)
golden_cross = cross & (sma_5_over_25 == True)
dead_cross = cross & (sma_5_over_25 == False)
brand_df['sma_5'] = brand_df.groupby(
level='code')['close'].rolling(window=5).mean().values
brand_df['sma_25'] = brand_df.groupby(
level='code')['close'].rolling(window=25).mean().values
brand_df['sma_5_over_25'] = sma_5_over_25.values
brand_df['golden_cross'] = golden_cross.values
brand_df['dead_cross'] = dead_cross.values
df = df.append(brand_df[25:], sort=True)
return df
def trade_func(brands, date, portfolio):
orders = []
# Dead crossが発生していて持っている株があれば売る
for brand in brands:
code = brand.code
if date in price_df.loc[code].index and price_df.loc[code, date].dead_cross \
and code in portfolio.stocks:
print("sell")
orders.append(
SellMarketOrder(code,
portfolio.stocks[code].current_count))
# 保有していない株でgolden crossが発生していたら買う
if brand in brands:
code = brand.code
unit = brand.unit
if date in price_df.loc[code].index and price_df.loc[code, date].golden_cross \
and code not in portfolio.stocks:
print("buy")
orders.append(BuyMarketOrderAsPossible(code, unit))
return orders
def get_open_price_func(code, date):
return price_df.loc[code, date].open
def get_close_price_func(code, date):
return price_df.loc[code, date].close
def current_price(d):
# 本日(d)の損益などを記録
current_total_price = portfolio.calc_current_total_price(
lambda code, date: get_close_price_func(code, date), d)
return current_total_price, current_total_price - portfolio.amount_of_investment
total_price_list.append(current_total_price)
profit_or_loss_list.append(current_total_price -
portfolio.amount_of_investment)
def simulate(portfolio, df):
def execute_order(d, orders):
# 本日(d)において注文(orders)をすべて執行する
for order in orders:
executions.append(
order.execute(d, portfolio, get_open_price_func))
orders = []
total_price_list = []
profit_or_loss_list = []
executions = []
for current_date in date_range:
# シミュレーション
execute_order(current_date, orders)
total_price, profit_or_loss = current_price(current_date)
total_price_list.append(total_price)
profit_or_loss_list.append(profit_or_loss)
orders = trade_func(brand_list, current_date, portfolio)
return total_price_list, profit_or_loss_list, executions
portfolio = Portfolio(10000000)
brand_list = list(BrandRepo().select_all_brand())
price_df = make_price_df(brand_list)
date_range = [
pdate.to_pydatetime().date() for pdate in tse_date_range(
price_df.sort_values(by='date', ascending=True).head(
1).index.get_level_values('date')[0],
price_df.sort_values(by='date', ascending=False).head(
1).index.get_level_values('date')[0])
]
total_price_list, profit_or_loss_list, executions = simulate(
portfolio, price_df)
profit_df = pd.DataFrame({
'date': date_range,
'profit_or_loss': profit_or_loss_list
}).set_index('date')
save_dataframe(profit_df, 'temp/simulate.png')
execution_str = ""
for execution in executions:
execution_str += str(execution) + "\n"
slack_comment(execution_str)
slack_file_upload('temp/simulate.png')
class TestBrandRepo(TestCase):
def __init__(self, *args, **kwargs):
super(TestBrandRepo, self).__init__(*args, **kwargs)
self.brandRepo = BrandRepo()
BrandRepo()
BrandRepo()
BrandRepo()
def test(self):
print("Test BrandRepo start")
self.brandRepo.setup()
# 一旦全削除
self.brandRepo.delete_all()
self.checkBrand()
self.checkNewBrand()
print("Test BrandRepo end")
def checkBrand(self):
print("Test Brand start")
brands1 = [
Brand(code="3382",
name="セブン＆アイ・ホールディングス",
short_name="セブン＆アイ",
market="東証１",
sector="小売業",
unit=100),
Brand(code="4755",
name="楽天",
short_name="楽天",
market="東証１",
sector="サービス業",
unit=100),
Brand(code="6861",
name="キーエンス",
short_name="キーエンス",
market="東証１",
sector="電気機器",
unit=100)
]
self.brandRepo.insert_brands(brands1)
self.brandRepo.insert_brands(brands1)
brands2 = [
Brand(code="6752",
name="パナソニック",
short_name="パナソニック",
market="東証１",
sector="電気機器",
unit=100),
Brand(code="6861",
name="キーエンス",
short_name="キーエンス",
market="東証１",
sector="電気機器",
unit=100)
]
self.brandRepo.insert_brands(brands2)
print("Test Brand end")
def checkNewBrand(self):
this_year1 = datetime.now()
this_year2 = this_year1 + timedelta(days=1)
this_year3 = this_year2 + timedelta(days=1)
last_year1 = this_year1 - timedelta(days=365)
last_year2 = last_year1 + timedelta(days=1)
two_years_ago1 = this_year1 - timedelta(days=730)
print("Test newBrand start")
newBrands1 = [
NewBrand("3000", this_year1.strftime('%Y/%m/%d'), "会社A"),
NewBrand("3001", this_year2.strftime('%Y/%m/%d'), "会社B"),
NewBrand("3002", this_year3.strftime('%Y/%m/%d'), "会社C"),
NewBrand("3003", last_year1.strftime('%Y/%m/%d'), "会社D")
]
self.brandRepo.insert_new_brands(newBrands1)
self.brandRepo.insert_new_brands(newBrands1)
newBrands2 = [
NewBrand("3002", this_year3.strftime('%Y/%m/%d'), "会社C"),
NewBrand("3003", last_year1.strftime('%Y/%m/%d'), "会社D"),
NewBrand("3004", last_year2.strftime('%Y/%m/%d'), "会社E"),
NewBrand("3005", two_years_ago1.strftime('%Y/%m/%d'), "会社F")
]
self.brandRepo.insert_new_brands(newBrands2)
dic = dict(
list(
map(lambda newBrand: (newBrand.code, newBrand),
self.brandRepo.select_new_brand_recently())))
self.assertTrue("3000" in dic)
self.assertTrue(
dic["3000"].format_date() == this_year1.strftime('%Y/%m/%d'))
self.assertTrue("3001" in dic)
self.assertTrue(
dic["3001"].format_date() == this_year2.strftime('%Y/%m/%d'))
self.assertTrue("3002" in dic)
self.assertTrue(
dic["3002"].format_date() == this_year3.strftime('%Y/%m/%d'))
self.assertTrue("3003" in dic)
self.assertTrue(
dic["3003"].format_date() == last_year1.strftime('%Y/%m/%d'))
self.assertTrue("3004" in dic)
self.assertTrue(
dic["3004"].format_date() == last_year2.strftime('%Y/%m/%d'))
self.assertFalse("3005" in dic)
print("Test newBrand end")
def __init__(self, *args, **kwargs):
super(TestBrandRepo, self).__init__(*args, **kwargs)
self.brandRepo = BrandRepo()
BrandRepo()
BrandRepo()
BrandRepo()