Newbie dtformat
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Hello everybody,
I'm a newbie and I can't solve the attached code issue.
I think the problem is dtformat='%Y-%m-%d %H:%M:%S'.
But I don't get over it.I take the opportunity to ask you if you know where to find various example codes for a neophyte?
======================================
EURUSD_1_H_csv_parsed = bt.feeds.PandasData (dataname=EURUSD_1_H_csv,
datetime=None,
open=0,
high=1,
low=2,
close=3,
volume=None,
openinterest=None,dtformat='%Y-%m-%d %H:%M:%S' )print(type(EURUSD_1_H_csv_parsed))
cerebro = bt.Cerebro()
cerebro.adddata(EURUSD_1_H_csv_parsed)
cerebro.run()
%matplotlib inline
cerebro.plot(iplot=False)========================================
TypeError: PandasData.init() got an unexpected keyword argument 'dtformat'Many thanks
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@tavagnascoi here is my code:
data = btfeeds.GenericCSVData( dataname=file_path, nullvalue=0.0, dtformat=('%Y-%m-%d'), datetime=0, open=1, high=2, low=3, close=4, volume=5, openinterest=-1 )Seems like you need to put the dtformat's value in parentheses? Let me know if that fixes it. I am a newbie as well.
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@Z This is the solution I found:
I apologize in advance for the long text!=================
df1 = pd.read_csv(name_file)
df1['time'] = pd.to_datetime(df1['time'])
df1['time'] = df1['time'].dt.strftime('%Y-%m-%d %H:%M:%S')
df1['time'] = pd.to_datetime(df1['time']) # Same code, in this way, work ?
df1.set_index("time", inplace=True)=========================
This code below works.
Adding an indicator.=========================
from future import (absolute_import, division, print_function,
unicode_literals)import datetime # For datetime objects
import os.path # To manage paths
import sys # To find out the script name (in argv[0])# Import the backtrader platform
import backtrader as bt
# Create a Stratey
class TestStrategy(bt.Strategy):
params = (
('maperiod', 15),
)def log(self, txt, dt=None): ''' Logging function fot this strategy''' dt = dt or self.datas[0].datetime.date(0) print('%s, %s' % (dt.isoformat(), txt)) def __init__(self): # Keep a reference to the "close" line in the data[0] dataseries self.dataclose = self.datas[0].close # To keep track of pending orders and buy price/commission self.order = None self.buyprice = None self.buycomm = None # Add a MovingAverageSimple indicator self.sma = bt.indicators.SimpleMovingAverage( self.datas[0], period=self.params.maperiod) def notify_order(self, order): if order.status in [order.Submitted, order.Accepted]: # Buy/Sell order submitted/accepted to/by broker - Nothing to do return # Check if an order has been completed # Attention: broker could reject order if not enough cash if order.status in [order.Completed]: if order.isbuy(): self.log( 'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' % (order.executed.price, order.executed.value, order.executed.comm)) self.buyprice = order.executed.price self.buycomm = order.executed.comm else: # Sell self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' % (order.executed.price, order.executed.value, order.executed.comm)) self.bar_executed = len(self) elif order.status in [order.Canceled, order.Margin, order.Rejected]: self.log('Order Canceled/Margin/Rejected') self.order = None def notify_trade(self, trade): if not trade.isclosed: return self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' % (trade.pnl, trade.pnlcomm)) def next(self): # Simply log the closing price of the series from the reference self.log('Close, %.2f' % self.dataclose[0]) # Check if an order is pending ... if yes, we cannot send a 2nd one if self.order: return # Check if we are in the market if not self.position: # Not yet ... we MIGHT BUY if ... if self.dataclose[0] > self.sma[0]: # BUY, BUY, BUY!!! (with all possible default parameters) self.log('BUY CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order = self.buy() else: if self.dataclose[0] < self.sma[0]: # SELL, SELL, SELL!!! (with all possible default parameters) self.log('SELL CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order = self.sell()if name == 'main':
# Create a cerebro entity
cerebro = bt.Cerebro()# Add a strategy cerebro.addstrategy(TestStrategy) data = bt.feeds.PandasData (dataname=df1, datetime=None, open=0, high=1, low=2, close=3, volume=None, openinterest=None, #dtformat= ('%Y-%m-%d %H:%M:%S') ) # Add the Data Feed to Cerebro cerebro.adddata(data) # Set our desired cash start cerebro.broker.setcash(1000.0) # Add a FixedSize sizer according to the stake cerebro.addsizer(bt.sizers.FixedSize, stake=10) # Set the commission cerebro.broker.setcommission(commission=0.0) # Print out the starting conditions print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue()) # Run over everything cerebro.run() # Print out the final result print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())###=======================
This code below works.
Let’s Optimize.
###=======================
from future import (absolute_import, division, print_function,
unicode_literals)import datetime # For datetime objects
import os.path # To manage paths
import sys # To find out the script name (in argv[0])# Import the backtrader platform
import backtrader as bt
# Create a Stratey
class TestStrategy(bt.Strategy):
params = (
('maperiod', 15),
('printlog', False),
)def log(self, txt, dt=None, doprint=False): ''' Logging function fot this strategy''' if self.params.printlog or doprint: dt = dt or self.datas[0].datetime.date(0) print('%s, %s' % (dt.isoformat(), txt)) def __init__(self): # Keep a reference to the "close" line in the data[0] dataseries self.dataclose = self.datas[0].close # To keep track of pending orders and buy price/commission self.order = None self.buyprice = None self.buycomm = None # Add a MovingAverageSimple indicator self.sma = bt.indicators.SimpleMovingAverage( self.datas[0], period=self.params.maperiod) def notify_order(self, order): if order.status in [order.Submitted, order.Accepted]: # Buy/Sell order submitted/accepted to/by broker - Nothing to do return # Check if an order has been completed # Attention: broker could reject order if not enough cash if order.status in [order.Completed]: if order.isbuy(): self.log( 'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' % (order.executed.price, order.executed.value, order.executed.comm)) self.buyprice = order.executed.price self.buycomm = order.executed.comm else: # Sell self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' % (order.executed.price, order.executed.value, order.executed.comm)) self.bar_executed = len(self) elif order.status in [order.Canceled, order.Margin, order.Rejected]: self.log('Order Canceled/Margin/Rejected') # Write down: no pending order self.order = None def notify_trade(self, trade): if not trade.isclosed: return self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' % (trade.pnl, trade.pnlcomm)) def next(self): # Simply log the closing price of the series from the reference self.log('Close, %.2f' % self.dataclose[0]) # Check if an order is pending ... if yes, we cannot send a 2nd one if self.order: return # Check if we are in the market if not self.position: # Not yet ... we MIGHT BUY if ... if self.dataclose[0] > self.sma[0]: # BUY, BUY, BUY!!! (with all possible default parameters) self.log('BUY CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order = self.buy() else: if self.dataclose[0] < self.sma[0]: # SELL, SELL, SELL!!! (with all possible default parameters) self.log('SELL CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order = self.sell() def stop(self): self.log('(MA Period %2d) Ending Value %.2f' % (self.params.maperiod, self.broker.getvalue()), doprint=True)if name == 'main':
# Create a cerebro entity
cerebro = bt.Cerebro()# Add a strategy strats = cerebro.optstrategy( TestStrategy, maperiod=range(10, 31)) data = bt.feeds.PandasData (dataname=df1, datetime=None, open=0, high=1, low=2, close=3, volume=None, openinterest=None, #dtformat= ('%Y-%m-%d %H:%M:%S') ) # Add the Data Feed to Cerebro cerebro.adddata(data) # Set our desired cash start cerebro.broker.setcash(1000.0) # Add a FixedSize sizer according to the stake cerebro.addsizer(bt.sizers.FixedSize, stake=10) # Set the commission cerebro.broker.setcommission(commission=0.0) # Run over everything cerebro.run(maxcpus=1)====================
This code below DOESN'T WORK, and I don't understand where the error is !?!?!?
Visual Inspection: Plotting ???====================
Error code:
ValueError: Axis limits cannot be NaN or Inf====================
from future import (absolute_import, division, print_function,
unicode_literals)import datetime # For datetime objects
import os.path # To manage paths
import sys # To find out the script name (in argv[0])# Import the backtrader platform
import backtrader as bt
# Create a Strategy
class TestStrategy(bt.Strategy):
params = (
('maperiod', 15),
)def log(self, txt, dt=None): ''' Logging function fot this strategy''' dt = dt or self.datas[0].datetime.date(0) print('%s, %s' % (dt.isoformat(), txt)) def __init__(self): # Keep a reference to the "close" line in the data[0] dataseries self.dataclose = self.datas[0].close # To keep track of pending orders and buy price/commission self.order = None self.buyprice = None self.buycomm = None # Add a MovingAverageSimple indicator self.sma = bt.indicators.SimpleMovingAverage( self.datas[0], period=self.params.maperiod) # Indicators for the plotting show bt.indicators.ExponentialMovingAverage(self.datas[0], period=25) bt.indicators.WeightedMovingAverage(self.datas[0], period=25, subplot=True) bt.indicators.StochasticSlow(self.datas[0]) bt.indicators.MACDHisto(self.datas[0]) rsi = bt.indicators.RSI(self.datas[0]) bt.indicators.SmoothedMovingAverage(rsi, period=10) bt.indicators.ATR(self.datas[0], plot=False) def notify_order(self, order): if order.status in [order.Submitted, order.Accepted]: # Buy/Sell order submitted/accepted to/by broker - Nothing to do return # Check if an order has been completed # Attention: broker could reject order if not enough cash if order.status in [order.Completed]: if order.isbuy(): self.log( 'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' % (order.executed.price, order.executed.value, order.executed.comm)) self.buyprice = order.executed.price self.buycomm = order.executed.comm else: # Sell self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' % (order.executed.price, order.executed.value, order.executed.comm)) self.bar_executed = len(self) elif order.status in [order.Canceled, order.Margin, order.Rejected]: self.log('Order Canceled/Margin/Rejected') # Write down: no pending order self.order = None def notify_trade(self, trade): if not trade.isclosed: return self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' % (trade.pnl, trade.pnlcomm)) def next(self): # Simply log the closing price of the series from the reference self.log('Close, %.2f' % self.dataclose[0]) # Check if an order is pending ... if yes, we cannot send a 2nd one if self.order: return # Check if we are in the market if not self.position: # Not yet ... we MIGHT BUY if ... if self.dataclose[0] > self.sma[0]: # BUY, BUY, BUY!!! (with all possible default parameters) self.log('BUY CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order = self.buy() else: if self.dataclose[0] < self.sma[0]: # SELL, SELL, SELL!!! (with all possible default parameters) self.log('SELL CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order = self.sell()if name == 'main':
# Create a cerebro entity
cerebro = bt.Cerebro()# Add a strategy cerebro.addstrategy(TestStrategy) data = bt.feeds.PandasData (dataname=df1, datetime=None, open=0, high=1, low=2, close=3, volume=None, openinterest=None, #dtformat= ('%Y-%m-%d %H:%M:%S') ) # Add the Data Feed to Cerebro cerebro.adddata(data) # Set our desired cash start cerebro.broker.setcash(1000.0) # Add a FixedSize sizer according to the stake cerebro.addsizer(bt.sizers.FixedSize, stake=10) # Set the commission cerebro.broker.setcommission(commission=0.0) # Print out the starting conditions print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue()) # Run over everything cerebro.run() # Print out the final result print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue()) # Plot the result cerebro.plot()
