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please generate a MACD strategy
This class uses the MACD indicator’s components (macd line, signal line) and crossover signals to inform buy/sell decisions. Remember to set your backtesting configurations and live trading cofigurations appropriately when using this strategy.
Thanks, now please modify my code to make the macd1 equal to 10, and use a different Crossover indicator
Okay, I’ll update your ‘MACDStrategy code with ‘macd1’ set to 10 and use a differnet crossover indicator. Instead of using ‘bt.indicators.CrossOver’ and ‘bt.indicators.CrossDown’, I’ll implement a custom logic for the crossover by comparing the current and previous values of the MACD line and signal line. Here is your modified code:
class MACDStrategy(bt.Strategy):
params = (
('macd1', 21),
('macd2', 26),
('macd3', 9),
)
def log(self, txt, order):
dt = dt or self.data.datetime.date(0)
print(f'{dt.isoformat()} {txt}')
def __init__(self):
self.macd = bt.indicators.MACD(
self.data.close,
period_me1=self.params.macd1,
period_me2=self.params.macd2,
period_signal=self.params.macdsig
)
self.crossup = bt.indicators.CrossOver(
self.macd.macd, self.macd.signal
)
self.crossdown = bt.indicators.CrossDown(
self.macd.macd, self.macd.signal
)
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Whether you are new to Python or not, you will find your code easy to read and edit in case you need to tweak some parameters. TradeLocker Studio gives you the possibility to use a vast array of libraries for whatever you set your mind to.
import math
import backtrader as bt
class MeanReversionStrategy(bt.Strategy):
params = (('period', 25),)
def __init__(self):
self.executed_prices = []
def notify_order(self, order):
if order.status == order.Completed:
self.executed_prices.append(order.executed.price)
self.log(f'{"BUY" if order.isbuy() else "SELL"} EXECUTED, Price: {order.executed.price:.2f}, Cost: {order.executed.value:.2f}, Comm {order.executed.comm:.2f}')
self.order = None
if self.executed_prices:
geometric_mean_price = math.exp(sum(math.log(p) for p in self.executed_prices) / len(self.executed_prices))
self.log(f'Geometric Mean of Prices: {geometric_mean_price:.2f}')
def log(self, txt, dt=None):
dt = dt or self.datas[0].datetime.date(0)
print(f'{dt.isoformat()}, {txt}')
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