def __init__(self):
"""
docstring
"""
self.db = Database()
self.df = pd.DataFrame()
self.bt = BackTest()
def __init__(self):
"""
docstring
"""
self.bs = BuySell()
self.db = Database()
self.bt = BackTest()
def __init__(self):
self.db = Database()
self.bs = BuySell(
trading_strategy=TradingStrategies.LOGISTIC_REGRESSION,
trading_provider=TradingProviders.ALPACA,
fill_open_trades=OpenTradesSource.DB)
self.bs.close_alpaca_postition = True
self.spy_limit = 0
def __init__(self):
self.db = Database()
self.symbols = self.db.get_symbols(TableName.DAY)
self.sectors = self.db.get_sectors(TableName.DAY)
self.sm = StockMess()
self.app = self.get_home_page()
self.sw = StockWhisperer()
self.submit = None
def __init__(self):
self.db = Database()
self.df = self.db.load_data("p_day",
symbols=['SPY'],
time_from="-120d")
self.df = sdf.retype(self.df)
self.df = FinI.add_indicators(self.df)
self.app = self.get_home_page(self.df)
self.fig = None
def update_figure(input_value):
if len(input_value) > 1:
input_value = "SPY"
db = Database()
df = db.load_data("p_day", symbols=input_value, time_from="-120d")
df = sdf.retype(df)
df = FinI.add_indicators(df)
fig = go.Figure()
fig.add_trace(
go.Candlestick(x=df.index,
open=df['open'],
high=df['high'],
low=df['low'],
close=df['close']), )
fig.add_trace(go.Scatter(x=df.index, y=df.sma9))
fig.update_layout(transition_duration=500)
return fig
def __init__(self) -> None:
db = Database()
stocks: pd.DataFrame
stocks_fs = db.load_data(table_name=TableName.DAY_FS, limit=1)
db_mode = "append"
if stocks_fs is not None and len(stocks_fs) > 0:
stocks = db.load_data(table_name=TableName.DAY,
time_from=stocks_fs.index[0])
else:
stocks = db.load_data(table_name=TableName.DAY, time_from="-500d")
db_mode = "replace"
symbols = db.get_symbols()
for sym in symbols:
if len(stocks) > 0:
stocks_sym = FinI.add_indicators(stocks[stocks.sym == sym])
# stocks.columns.drop(["symbol","sector","industry"])
# for idx, sym_s in stocks_sym.iterrows():
# sql_string = "".join(["select * from financials where symbol = '",
# str(sym_s.sym),
# "' and date::date <= date '",
# str(idx.replace(hour=23, minute=59)),
# "' order by date desc limit 1"])
# print(sql_string)
# financials = db.sql_select_query_data(sql_string)
# new_row = pd.concat([stocks_sym, financials], axis=1)
# new_row.to_sql(name="p_day_fs",
# if_exists="append", con=db.engine)
if stocks_sym is not None:
stocks_sym.to_sql(name="p_day_fs",
if_exists=db_mode,
con=db.engine)
print(stocks_sym)
utc = pytz.UTC
# from buy_sell import BuySell
# from sklearn import linear_model, preprocessing
from pytz import timezone
localtz = timezone('Europe/Prague')
# from plot_i import PlotI
# from fin_i import FinI
# from utils import Utils
# from check_indicators import CheckIndicators as chi
from alpaca_examples.market_db import Database, TableName
from buy_sell import BuySell
import logging
# import pytest
db = Database()
bs = BuySell()
data_intc = db.load_data(table_name=TableName.DAY,
time_from="-10d",
symbols=["INTC"])
data_amd = db.load_data(table_name=TableName.DAY,
time_from="-10d",
symbols=["AMD"])
def test_buy_sell_stock_t():
bs.buy_sell_open = pd.DataFrame()
bs.money = 10000
bs.buy_stock_t(data_intc.iloc[0],
data_intc.iloc[0].close,
def __init__(self):
"""
docstring
"""
self.db = Database()