| | --- |
| | title: LensIQ |
| | emoji: 🏢 |
| | colorFrom: gray |
| | colorTo: pink |
| | sdk: docker |
| | app_port: 7860 |
| | pinned: false |
| | short_description: CAPM + CML portfolio advisor w/ embeddings |
| | models: |
| | - FinLang/finance-embeddings-investopedia |
| | datasets: |
| | - Tulitula/LensIQ-Synthetic-Portfolio-1000 |
| | --- |
| | |
| | # LensIQ |
| |
|
| | - **Plot:** x = historical σ (annualized, from monthly covariances), y = CAPM E[r] = rf + β·ERP. |
| | - **Efficient mixes:** market/bills with (i) same σ, (ii) same E[r] as your portfolio (weights shown). |
| | - **Suggestions:** one Low/Medium/High, from 1,000 long-only mixes built **from your tickers** (+ VOO), ranked with CAPM + embeddings (MMR). |
| | - **Export:** every run writes a CSV of all 1,000 candidates in `data/`. |
| |
|
| | > Include **VOO** in your portfolio before computing. |
| |
|
| | ## Run Locally |
| |
|
| | ```bash |
| | docker build -t lensiq . |
| | docker run -p 7860:7860 -e GRADIO_SERVER_NAME=0.0.0.0 lensiq |
| | # open http://localhost:7860 |
| | Create & Upload the Dataset |
| | In the app, add tickers, set amounts, horizon, lookback → Compute. |
| | |
| | Download the generated CSV from the UI (saved under data/). |
| | |
| | Create a dataset repo on Hugging Face named Tulitula/LensIQ-Synthetic-Portfolio-1000 and upload: |
| | |
| | the CSV you downloaded |
| | |
| | the dataset README from below |
| | |
| | |
| | **B) Dataset README (for `Tulitula/LensIQ-Synthetic-Portfolio-1000`)** |
| | ```markdown |
| | # LensIQ Synthetic Portfolio 1000 |
| |
|
| | Synthetic portfolio dataset generated by the LensIQ app for CAPM/CML-based analysis and ranking. |
| |
|
| | - **Size:** 1,000 rows (per export) |
| | - **Modality:** Tabular (text fields for lists) |
| | - **License:** CC BY 4.0 |
| | - **Intended use:** Research/teaching on portfolio construction, risk/return modeling, ranking/retrieval. *Not financial advice.* |
| |
|
| | ## Generation Process |
| |
|
| | Given a user universe **U** (their tickers + `VOO` as market): |
| |
|
| | 1. Sample `k ~ U{2…min(8, |U|)}` tickers from **U** (no replacement). |
| | 2. Draw long-only weights `w ~ Dirichlet(1,…,1)`. |
| | 3. Compute: |
| | - Prices: Yahoo Finance monthly (auto-adjusted), last *N* years (lookback). |
| | - Risk-free `rf`: FRED tenor chosen by horizon (e.g., DGS10). |
| | - Market `m`: `VOO`. |
| | - Market ERP `= E[m] − rf` (from monthly returns, annualized). |
| | - Asset betas vs. market (excess returns). |
| | - **mu_capm** `= rf + β_p·ERP`, where `β_p = Σ_i w_i β_i`. |
| | - **sigma_hist**: annualized portfolio σ from monthly covariance. |
| | - **sigma_capm**: `|β_p| · σ_market` (for reference to CML). |
| | 4. Rank candidates with CAPM metrics + finance embeddings (MMR) to surface Low/Medium/High picks in the app. |
| | |
| | ## Columns |
| | |
| | | column | type | description | |
| | |--------------|--------|--------------------------------------------------------| |
| | | `tickers` | string | comma-separated tickers in the candidate mix | |
| | | `weights` | string | comma-separated floats summing to 1 (long-only) | |
| | | `beta` | float | portfolio beta (weighted by `weights`) | |
| | | `mu_capm` | float | CAPM expected annual return | |
| | | `sigma_hist` | float | annualized historical σ from covariance | |
| | | `sigma_capm` | float | CAPM σ on the CML for equivalent return | |
| | |
| | **Example row** |
| | tickers: "VOO,QQQ,IBIT" |
| | weights: "0.53,0.41,0.06" |
| | beta: 1.18 |
| | mu_capm: 0.153 |
| | sigma_hist: 0.209 |
| | sigma_capm: 0.191 |
| | |
| | |
| | ## Files |
| | |
| | - `investor_profiles_<timestamp>.csv` — 1,000 synthetic candidates. |
| | |
