( in Quantitative Finance, AI, ML, ... ) A systematic approach to put a System-under-Test [ SuT ] into a state, where historical data ( a known part of both the evolution of inputs and the eco-system responses ) are used & fed into a SuT so as to review it's behaviour in-vitro ( as opposed to a forward-testing )
( in Quantitative Finance
, AI
, ML
, Technical Cybernetics
, Modelling
... )
Back-testing
is a systematic approach to put a System-under-Test
[ SuT
] into a state, where historical data ( as a known part of both the evolution of inputs and the eco-system responses ) are used & fed into a SuT
so as to review it's behaviour in-vitro ( as opposed to a forward-testing ).
Strengths & Weaknesses
The concept of back-testing relies on an a-priori belief ( a proof of which is left on the reader ) that the SuT
does not intervene with the environment's generalised dynamics.
In other words, back-testing assumes that the SuT
does not influence the future evolution moves ( the very steps that were recorded in the Historical Data
, that are being fed into the SuT
during the flow of back-testing ) that the outer ecosystem, surrounding the SuT
, actually undertook in the past.
Tools
Strategy Tester,
Quantopian,
Quant Modeller,
Quant Strat,
AmiBroker,
TradingView,
MultiCharts
and many more
Historical Data
Quandl,
Olsen Data,
Dukas Copy,
Yahoo! Finance,
Google
and many more