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import matplotlib.pyplot as plt
import pandas as pd # type: ignore
import logging
from auto_trading.broker.backtest import Backtest
from auto_trading.indicators.dumb import Dumb
from auto_trading.indicators.ema2 import EMA
from auto_trading.indicators.slopy import Slopy
from auto_trading.indicators.sma2 import SMA
from auto_trading.interfaces import Indicator
from auto_trading.strat.buyupselldown import BuyUpSellDown
from auto_trading.strat.hold import Hold
from auto_trading.ptf.in_memory import InMemoryPortfolio
from auto_trading.bot import Bot
pd.options.plotting.backend = "plotly"
logging.basicConfig(level=logging.INFO)
if __name__ == "__main__":
bt = Backtest("./data/NYSE_small.csv")
ptf = InMemoryPortfolio(
base_balance=100, change_rate_getter=lambda: bt.current_change
)
strategy = BuyUpSellDown({"ema": EMA(alpha=0.6), "sma": SMA(windowSize=5)})
bot = Bot(ptf, strategy, bt)
bot.run()
###### Visualisation du Dataset ######
ANALYSE_DATASET = False
if ANALYSE_DATASET:
data = bt.data.unstack()
# select high prices for each action
closeData = data["close"]
sma = closeData.rolling(window=5).mean()
ema = closeData.ewm(alpha=0.6).mean()
plt.plot(closeData, label="real", marker="x", linewidth=0)
plt.plot(sma, color="red", label="SMA")
plt.plot(ema, color="green", label="EMA")
plt.legend()
plt.show()
print(bot.ptf.balance)
print(bot.ptf.total_balance(bot.broker.current_change))
ch_history: pd.DataFrame = bot.broker.change_rate_history # type: ignore
st_history = pd.DataFrame(
[s.stocks for s in bot.ptf.states_history],
index=ch_history.index,
)
(st_history * ch_history).fillna(0).plot.area().show()