| # MiDM financial architecture and application | |
| [Live ScenarioView report](https://yeohoonyun.github.io/midm-decision-models/) | |
| ## Active architecture versus experiments | |
| The active model is unchanged: Qwen3.5-9B-Base + trained LoRA adapter + joint-option pointer head. B3 produces seven scenario scores, supplied alongside as-of market/position features in the MiDM prompt. MiDM chooses between LONG/CASH (BUY/WAIT or HOLD/SELL according to position). Price-based risk/expiry rules are implemented separately. The report writer and rendering templates are separate from this choice model. | |
| ```text | |
| Local prices -> B3 7-case scores -> structured state and options | |
| | | |
| frozen MiDM 9B + pointer | |
| | | |
| LONG/CASH choice -> risk rules | |
| | | |
| local report -> public aggregates | |
| Experimental branch (NOT active): | |
| numeric context / frozen latent features -> small residual head | |
| + clipped MiDM logit -> sigmoid -> choice | |
| ``` | |
| ## Implemented residual architecture | |
| The accompanying residual_heads.py contains the actual architecture used in the experiment, separated from private paths and datasets. Dependencies: NumPy and SciPy. Do not load private training pickles from untrusted sources. | |
| - Numeric input: 24 fields; six engineered fields add case entropy, top-two gap, two volatility-normalized returns, trend alignment and clipped MiDM margin. | |
| - Numeric residual: 31 coefficients including intercept. | |
| - Horizon/position residual: 91 coefficients, with interactions for 20-day versus 5-day horizon and held versus flat state. | |
| - Latent residual: 39 coefficients; adds eight PCA components from 128-dimensional frozen projected hidden features. PCA and scaling are fitted only in training folds. | |
| - Random-feature comparator: 63 coefficients; 32 fixed cosine features, without a MiDM logit offset. Its inputs still contain the MiDM margin, so this is NOT an entirely MiDM-independent model. | |
| - Residual probability: sigmoid(clip(MiDM margin, -6, 6) + w^T x). Raw baseline retains its original logits. L2 penalties 0.1, 1, 10; all coefficients including intercept are penalized. | |
| Numeric columns: ret1, ret5, ret20, ret60, annualized rv20, drawdown252, ma20gap, ma50gap, ma200gap, rv20change5, age_sessions, remaining_sessions, entry_pnl, support_distance, two_closes_below_ratcheted_support, held, horizon/20, RT1 through RT7. Mapping and units must match before calling the head. `d` contains numeric[N,24], latent[N,128], margin[N], and (training only) y[N], with LONG=1. No raw training data or trained financial head weights are included. | |
| ## Application evidence | |
| KOSPI action choice, 5/20-day horizons, 756 hypothetical states across 129 dates (2024-01-02–2026-08-20). Expanding development years 2020–2023; 60-calendar-day purge plus label-expiry filter. Final head fit includes purged 2023; differences from earlier experiments are not architecture-only effects. This retrospective period had already been inspected. | |
| | Model | Accuracy | Balanced accuracy | | |
| |---|---:|---:| | |
| | Existing MiDM | 54.37% | 49.24% | | |
| | Numeric residual | 48.81% | 48.29% | | |
| | Horizon/position residual | 47.49% | 47.75% | | |
| | Latent residual | 50.53% | 49.98% | | |
| | Random-feature comparator | 48.41% | 51.72% | | |
| No financial candidate was promoted. This is implementation and negative-result documentation, not an improved-weight release. The current multi-asset application now executes MiDM locally for each asset and both horizons; stale source dates remain explicitly labeled. Prepared uncertainty/context prompts have not yet been inference-tested. The static report shows public macro references, case definitions, aggregate evidence and provenance; it does not disclose private per-date scores. | |
| ## Current application | |
| The [Korean report collection](https://yeohoonyun.github.io/midm-decision-models/index.ko.html) and [English report collection](https://yeohoonyun.github.io/midm-decision-models/index.en.html) uses new local MiDM inference for 12 assets and 48 hypothetical action states (5/20-day, flat/held). Asset-matched B3 is used for KOSPI and S&P 500; the individual stocks use causal price features and historical-analogue counts, explicitly not neural B3 probabilities. Financial transfer quality is not validated separately for each asset. Generation scripts and private financial weights/data are not published. | |