@rm said in Datafeed as indicator:

preprocessed data > original data

That's a standard comparison and will generate a lines object. Same as if you did:

data.close - data.high

It doesn't matter if the operation is boolean or arithmentic.

Plotting a vertical line is actually not there, neither on the data nor on the indicator. Your best bets:

Having an observer plotting markers on the data based on the value of an indicator. The BuySell observer looks into buy/sell orders to plot the markers. This would allow you to plot on the data but would be the most ad-hoc solution.

Having an indicator plotting markers. For the markers you can look into the BuySell observer or the ParabolicSAR indicator. In your case and since you only want the marker when your indicator is 1, you only assign a value to the indicator when that's the case (and leave the 0 case untouched)

From BuySell

plotlines = dict( buy=dict(marker='^', markersize=8.0, color='lime', fillstyle='full', ls=''), sell=dict(marker='v', markersize=8.0, color='red', fillstyle='full', ls='') )

The marker values and options are defined by matplotlib. See how this Blog - Arrows for the BuySell Observer changes the markers for the BuySell observer

@backtrader

Will test it out. Thanks once again

@backtrader said in Wrong value of Indicator in another CustomIndicator:

Of course. You can pass any number of data-feed-like objects to indicators. They will be available as self.dataX or the corresponding self.datas[X]

Nice. For reference:

self.sr = SR(self.datas[0], self.atr)

Gives

0_1500206033324_aa7601ee-6719-4a33-a326-8accde974350-grafik.png

inside custom indicator SR. So it can be used like

class SR(bt.Indicator): def __init__(self, logger, master, fromdate, todate): # self.datas[0] is yahoo market data (as default) self.atr = self.datas[1]

DONE

That I thought, Pythonized:

upw = dtr==false and dtr[-1] and utr dnw = utr==false and dtr[-1] and dtr

Meaning upw is true if current dtr is false and utr is true and the previous dtr is false. This would mark an upward trend change.

The same

upw = (not dtr) & dtr[-1] & utr dnw = (not dtr) & dtr[-1] & utr

In Metastock:

Assignation :=
Comparison =

@GwadaMan said in Make calcul with indicator:

def __init__(self): self.sma0 = btind.MovAv.SMA(self.data, period=12) self.sma1 = ((self.sma0.lines.sma(0) - self.sma0.lines.sma(1)) / 2)

A couple of comments for potential simplifications (some may for sure see it as more complex)

self.sma0 = btind.MovAv.SMA(self.data, period=12)

This can be usually simplified to:

self.sma0 = btind.MovAv.SMA(period=12)

When no specific data is specified, the 1st data of wherever the calculation is being made is implied.

self.sma1 = ((self.sma0.lines.sma(0) - self.sma0.lines.sma(-1)) / 2)

can be also be simplified to:

self.sma1 = ((self.sma0.lines.sma - self.sma0.lines.sma(-1)) / 2)

where there is no need to specify the (0), because the reference is implicit in the operation

Although @ntguardian is right and (1) points to the future, this can be used for real reasons. For example to push values from the future into the past as it is done in the Ichimoku indicator. For example:

... self.l.chikou_span = self.data.close(self.p.chikou) ...

This says that the current value of chikou_span has to be that of the future close (self.p.chikou has a default positive value of 26)

@Andre said in Starting strategy later while using get(ago,size):

which calls Average(self.data, period=self.p.period). This seems to affect how the Strategy class operate

No, it's not affecting how the Strategy operates. It simply informs the strategy about the minimum period contribution it has. See the following:

Docs - Operating the Platfomr Docs - Strategy

The Minimum Period concept is explained in both and what will be called when.

The Average indicator is documented here:

Docs - Indicator Reference (ordered alphabetically)

If you want to read the code look in indicators/basicops.py

A sincere recommendation again: read what you have before your eyes.

First:

For code/output blocks: Use ``` (aka backtick or grave accent) in a single line before and after the block. See: http://commonmark.org/help/

Or else your posts are unreadable.

Second:

@vivalavida1982 said in Arbitrage Strategy Test with Minute Data:

TypeError: unsupported operand type(s) for +: 'int' and 'str'

You are obviously passing an str where an int is expected.

Third.

@vivalavida1982 said in Arbitrage Strategy Test with Minute Data:

for i in self.data1['Date'] and self.data2['Date']:

This seems to be a pandas-like syntax expectation. backtrader is not pandas. The expectation of your statement there is really a mistery here. Data feeds (like other lines objects) support integer indexing. See Docs - Platform Concepts

These is a great solution. Thanks for taking into account my proposals and include them in this great framework.

buy is for buying sell is for selling

Both can obviously close an existing position if they are opposing each other. But if any of the above had intelligence to undo what the other do, how would you reverse a position?

close is for closing a position and will take into account the existing position.

They are documented in Docs - Strategy

You may also close a position with order_target_xxx by setting the target to 0. See Docs - Target Orders

And you can even close positions with sell if your position sizing is controlled with a Sizer or convert the same code to be a position reverser. See Docs - Sizers - Smart Staking

The code will probably close positions in many cases, where there isn't simply a signal (neither buy nor sell)

This indicator is now in the development branch

Thanks @backtrader

Thanks @backtrader

There is no such thing as a sample implementation.

You send an order to the market: self.buy(price=myprice, exectype=bt.Order.MyPreferredExecutionType) The order is completed and notified to notify_order(order) order.executed.price contains the actual execution price A new order is sent to act as a stop-loss order: self.sell(price=% of order.executed.price, exectype=probably Stop)

You want to use replay. The strategy doesn't actually run every minute but runs with every tick of the data, slowly recreating the day. The indicators attached to the replayed data are re-calculated automatically for the current day with each new tick (there is no need to recalculate the previous days)

See: Docs - Data Replaying

The length (len(data_in_use)) determines if the replaying has moved to a new day.

That's all in the link above: Docs - Platform Concepts

There is a section about indexing and how 0 is the current point in time. Last is the last produced value before the current point in time and addressable with -1

@ab_trader In theory yes, because there was a use case with spreads (which oscillate around 0). The problem there was that some of Python tests for existence would fail because the price was zero. It was dealt with a long time ago.

@Maxim-Korobov

Main package already has Highest and Lowest indicators.
docs - indicators ref

@backtrader

Thanks, I will take a look.

No. There isn't.