# -1 Modeling

Modeling "after -1" argues that ShezheaNET MLT_HFT outcome (A) is always false. Modeling "after -1" allows for reverse beneficial context modeling, meaning everything is considered false, even when the outcome is correct. We found a stochastically advantageous development considering the -1 model subject. If (A) is always false, meaning the Adjusted Parameter Demo Test will always fail it creates a feedback loop in the negative rewarding system, trim-tuning the parameter models. (A) as an outcome is always false, until (B), the 20x3 outcome is equal to (A). A having the same int as B, consider it here being the parameters (q/a/y/r/d), means there is a perfect probability of executing the past trading environment (TE).

Finding (q/a/y/r/d) allows for categorizing and finding patterns in different TEs, resulting in the possibility of specific training of submodels on top of the main MLT model. 1/9 TEs are categorized as the following in NQ:

Optimal 1/9 NQ Model (Sub-Model A=) -> oNQ9

Less optimal 1/9 NQ Model (Sub-Model A&) -> loNQ9

Zero optimal 1/9 NQ Model (Sub-Model A/) -> zoNQ9

Optimal 1/9 SeekDestroy NQ Model (Sub-Model A$) -> skNQ9 Multisided Fraction Executions in skNQx Preview: skHFT - Liquidity Flow for markets

(Broken 1/9 SeekDestroy NQ Model (Sub-Model A§) -> bskNQ9)

1/9 in this context means 1/9th of a second. We are releasing the detailed names of the models, to avoid misunderstandings in future press releases.

### oNQ9 MLT-HFT Backtesting via the -1 Method

Execution 1 with the outcome (A1) is false (/ has a negative result), resulting in the (B1) data-cluster with 20x3 outcome being (naturally because of -1) false as well, since (A) ≠ (B). This allows for an optimized finding of execution parameters (q/a/y/r/d) based on ticker parameters that were present before Execution 1. Note that the algorithm doesn't act according to the target to replicate (q/a/y/r/d) to the following Executions, but to categorize it in TEs. Here Execution 1 gets categorized as an Optimal 1/9 NQ Model, after finding the exact optimal parameters for Execution 1. TEs are nothing less than periods (usually less than 20 seconds). The MLT-HFT algorithm can now adapt to different TEs at a lightning-fast speed. This fixes HFT overfitting. The following concludes the basic framework behind this.

This is just the framework, not a functional code.

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