We propose a mathematical model of momentum risk-taking, which is essentially real-time risk management, and discuss its implementation: an automated momentum equity trading system  . Risk-taking is one of the key components of general decision-making, a challenge for artificial intelligence and machine learning ; stock markets are quite a test for any risk-management theories . We begin with a relatively simple continuous model of news impact on share-prices . In spite of its simplicity, it describes well the power growthof share-prices ,  periodicity, logarithmic periodicity, and the market phenomena like using price targets and profit-taking; Bessel and hypergeometric functions are used. Its discretization is performed, adjusted to discontinuous functions and aimed at stock charts, the main examples for us. An automated trading system based on our approachproved to be successful in extensive historical and real-time experiments. Its preimage is a new contract card game presented at the end, a combination of bridge and poker  .
We propose a mathematical model of momentum risk-taking, which is essentially real-time risk management, focused on short-term volatility of stock markets. Its implementation, an automated momentum equity trading system , proved to be successful in extensive historical and real-time experiments, discussed in the paper . Risk-taking is one of the key components of general decision-making, a challenge for artificial intelligence and machine learning . We begin with a relatively simple new algebraic-type theory of news impact on share-prices , which describes well their power growth, periodicity, logarithmic periodicity, and the market phenomena like price targets and profit-taking; Bessel and hypergeometric functions are used. Its discretization results in some tables of bids, expected returns for different investment horizons. This is a new approach; its preimage is a new contract card game presented at the end, a combination of bridge and poker . The relations to random processes and fractional Brownian motion are outlined. We provide a reasonably complete description of our AI-type trading system, but our ML procedures are not discussed much (a special version of those in neural networking is used) .
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and discuss its implementation:
focused on short-term volatility of stock markets. Its implementation,
A
, proved to be successful in extensive historical and real-time experiments, discussed in the paper
D
; stock markets are quite a test for any risk-management theories
R
continuous model
new algebraic-type theory
D
. In spite of its simplicity, it describes well the power growthof share-prices
A
which describes well their power growth,
D
using
R
is performed, adjusted to discontinuous functions and aimed at stock charts, the main examples for us. An automated trading system based on our approachproved to be successful in extensive historical and real-time experiments. Its
results in some tables of bids, expected returns for different investment horizons. This is a new approach; its
A
. The relations to random processes and fractional Brownian motion are outlined. We provide a reasonably complete description of our AI-type trading system, but our ML procedures are not discussed much (a special version of those in neural networking is used)