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import logging
from typing import List
import pandas as pd # type: ignore
from ..indicators.ema2 import EMA
from ..indicators.sma2 import SMA
from ..orders import Long, Short
from ..interfaces import Strategy, Order, PTFState
class BuyUpSellDown(Strategy):
"""Buy when indicators are green, sell when red."""
def __init__(self):
"""Init the class"""
super().__init__()
self.indicators = {
"ema": EMA(alpha=0.6),
"sma": SMA(windowSize=5)
}
def execute(
self, data: pd.DataFrame, indicators_results: pd.DataFrame, state: PTFState
) -> List[Order]:
"""Just hold the value [to_hold].
Args:
data (DataFrame): The Data broker output.
For each time and each stock give (high, low, open, close).
indicators_results (DataFrame): Indicator-Stock valuated float.
For each indicator and each stock give -1 if realy bad and +1 if realy good.
Returns:
List[Order]: A list of orders to execute.
"""
nb_stock_type = len(indicators_results.columns)
orders = []
# if I have some money
for stock_name in indicators_results.columns:
# for each stock
# I calculate the value of the stock
market_price = data.loc[data.index[-1][0]].close.to_dict().get(stock_name)
### compute trust
trust = 0
for (index, row) in indicators_results.iterrows():
trust += row[stock_name]
if market_price is None:
# retry later
continue
if trust > 0.1:
if (balance := state.balance) > 0:
amount = balance / (market_price * nb_stock_type)
# and I buy it all at market price
orders.append(
Long(stock=stock_name, amount=amount, price=market_price)
)
else:
print("A PU DE THUNE")
elif trust < -0.1:
# and I sell it all at market price
if state.stocks.get(stock_name) is not None:
orders.append(
Short(
stock=stock_name,
amount=state.stocks[stock_name],
price=market_price,
)
)
return orders