CODEWITHSUNDEEP
pythonpandastradingalgorithmic-trading
Al_Iskander
Al_Iskander

Reputation: 1001

How to code a simple signal logic in pandas dataframe?

I want to create a simple trading system signal table with a dataframe such as:

            Close   buy   sell
2011-01-31  50       0     0
2011-02-28  40       1     0
2011-03-31  50       0     0
2011-04-30  80       0    -1
2011-05-31  60       1     0
2011-06-30  50       1     0 
2011-07-31  20       0     0
2011-08-31  30       0    -1

The signals are generated in a following fashion:

df['buy'] = np.where( <condition> , 1, 0 )

The sell column is created in the same way.

The issue is the double buy signal on 2011-06-30, right after a previous one on 2011-05-31.

2011-05-31  60       1     0
2011-06-30  50       1     0

How can I prevent a new buy ( == 1 ) signal before it is closed with -1 in the df['sell'] column?

Upvotes: 2

Views: 2376

Answers (2)

Abhishek Kulkarni
Abhishek Kulkarni

Reputation: 511

I am bit late for the party but I hope this helps. I suppose you want alternate 1 and -1. Though not the best way, I suggest you add buy and sell signals and call the new column as trade signal. The trade signal column would have 0, 1 and -1 in an arbitrary fashion. The following code explains further steps.

import pandas as pd
import numpy as np
trade_signal= pd.DataFrame([1,1,0,0,-1,-1,1,1,-1])
ts_shifted=trade_signal.shift(1)
trade_rule=np.where(trade_signal!=ts_shifted,trade_signal,0)

Upvotes: 2

user3666197
user3666197

Reputation: 1

How can I prevent...?

Well, once in a need of something more complex, than a trivial numpy vectorised / broadcast / conditional operation alike the stated np.where( <condition> , 1, 0 ) one has to implement some custom-specific function, to handle those additional features, where Finite-State-Automata may well serve for such purposes.

But how to implement it?

Simply said, your conditions have grown beyond the expressivity of simple vectorised/broadcast matrix operations, the Trade-Management sub-system has started to have an internal-, a SEQ-behaviour and some additional rules for it's internal -transition(s) and -transition-restrictions.

One could hardly expect the general, universal tools, the numpy-matrices and pandas-DataFrame instances are, to be over-designed to support such problem-domain specific functionalities so as to prototype algo-trading strategy simulations.

Better choose some trading-specific tools to suit such need, or expect to have to implement a SEQ-behaviour iterator-function, that would facilitate the needed logic ( none of the smart numpy vectorised / broadcasting expressions or pandas one-liner will suffice here anymore ).

Scope?
for those really interested in a rapid prototyped Trading Strategy example, using a FSA-based implementation method, check the simplicity of such system design + built-in ( script-based ) automated conformance-testing capability + line numbers for an estimate of the code-span ( definitely not a numpy SLOC ): enter image description here

Upvotes: 0

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