Update to LONA™

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.codex-plugin/plugin.json →

.codex-plugin/plugin.json

− removed · + added

--- before
+++ after
@@ -2,7 +2,7 @@
   "author": {
     "name": "Mindsight Ventures SL"
   },
-  "description": "LONA\u2122 is a trading strategy validation framework for AI agents. Describe a strategy idea and your agent writes it in Python and backtests it on historical market data, all inside ChatGPT.\n\nGo beyond a single lucky backtest: optimize strategy parameters across every combination in a grid and see whether performance holds up. Review interactive reports with price candles, equity and drawdown curves, per-symbol statistics and the full trade log.\n\nLONA\u2122 runs historical simulations only. It does not place live trades or connect to brokerage accounts, and nothing it shows is investment advice.",
+  "description": "Create, backtest, optimize and walk-forward test trading strategies with LONA\u2122. Describe your logic in plain language, or bring your own Python code. Read, edit and version your strategies.\n\nSTRATEGIES\n\u2022 Turn ideas into Python strategies with immutable versioning.\n\u2022 Create and maintain your own strategy code.\n\nMARKET DATA\n\u2022 US and China stocks and ETFs, crypto, forex and mutual funds, with daily and intraday data where available.\n\u2022 Split- and dividend-adjusted equity data.\n\u2022 Use LONA\u2122 datasets or upload your own OHLCV CSV.\n\nBACKTESTING\n\u2022 Interactive candlestick charts, equity and drawdown curves, per-symbol statistics and a full trade log.\n\u2022 Buy & Hold and benchmark comparisons.\n\nOPTIMIZATION\n\u2022 Sweep parameter grids and compare combinations by performance metric.\n\u2022 Day-of-Week and Time-of-Day tests block new entries on a selected weekday or UTC time slot; existing positions can still exit.\n\u2022 Explore results with interactive 2D and 3D charts.\n\nWALK-FORWARD ANALYSIS\n\u2022 Test optimized parameters on historical data not used during optimization to help assess robustness and detect overfitting.\n\u2022 Rolling and anchored windows: optimize in-sample, then test out-of-sample.\n\u2022 Review stitched out-of-sample equity and drawdown, window schedules, efficiency ratios and the share of profitable windows.\n\nMETRICS\nRealized P&L, total return, CAGR, annualized Sharpe ratio, maximum drawdown, return volatility, Calmar ratio, recovery factor, profit factor, payoff ratio, expectancy, average trade, win rate and average bars held.\n\nHistorical simulations only. LONA\u2122 does not place live trades or connect to brokerage accounts. Nothing it shows is investment advice.",
   "interface": {
     "capabilities": [],
     "category": "Finance",
@@ -13,7 +13,7 @@
     ],
     "developerName": "Mindsight Ventures SL",
     "displayName": "LONA\u2122",
-    "longDescription": "LONA\u2122 is a trading strategy validation framework for AI agents. Describe a strategy idea and your agent writes it in Python and backtests it on historical market data, all inside ChatGPT.\n\nGo beyond a single lucky backtest: optimize strategy parameters across every combination in a grid and see whether performance holds up. Review interactive reports with price candles, equity and drawdown curves, per-symbol statistics and the full trade log.\n\nLONA\u2122 runs historical simulations only. It does not place live trades or connect to brokerage accounts, and nothing it shows is investment advice.",
+    "longDescription": "Create, backtest, optimize and walk-forward test trading strategies with LONA\u2122. Describe your logic in plain language, or bring your own Python code. Read, edit and version your strategies.\n\nSTRATEGIES\n\u2022 Turn ideas into Python strategies with immutable versioning.\n\u2022 Create and maintain your own strategy code.\n\nMARKET DATA\n\u2022 US and China stocks and ETFs, crypto, forex and mutual funds, with daily and intraday data where available.\n\u2022 Split- and dividend-adjusted equity data.\n\u2022 Use LONA\u2122 datasets or upload your own OHLCV CSV.\n\nBACKTESTING\n\u2022 Interactive candlestick charts, equity and drawdown curves, per-symbol statistics and a full trade log.\n\u2022 Buy & Hold and benchmark comparisons.\n\nOPTIMIZATION\n\u2022 Sweep parameter grids and compare combinations by performance metric.\n\u2022 Day-of-Week and Time-of-Day tests block new entries on a selected weekday or UTC time slot; existing positions can still exit.\n\u2022 Explore results with interactive 2D and 3D charts.\n\nWALK-FORWARD ANALYSIS\n\u2022 Test optimized parameters on historical data not used during optimization to help assess robustness and detect overfitting.\n\u2022 Rolling and anchored windows: optimize in-sample, then test out-of-sample.\n\u2022 Review stitched out-of-sample equity and drawdown, window schedules, efficiency ratios and the share of profitable windows.\n\nMETRICS\nRealized P&L, total return, CAGR, annualized Sharpe ratio, maximum drawdown, return volatility, Calmar ratio, recovery factor, profit factor, payoff ratio, expectancy, average trade, win rate and average bars held.\n\nHistorical simulations only. LONA\u2122 does not place live trades or connect to brokerage accounts. Nothing it shows is investment advice.",
     "privacyPolicyURL": "https://www.lona.agency/en/privacy",
     "shortDescription": "Backtest trading strategies",
     "supportURL": "https://www.lona.agency/en#contact-section",
@@ -25,7 +25,7 @@
     "composerIconDark": "./assets/logo-dark.svg"
   },
   "name": "app-694336b0c0948191a4ad234f9942885b",
-  "version": "3.0.1",
+  "version": "3.0.2",
   "extensions": {
     "com.openai": {
       "review": {
@@ -84,7 +84,7 @@
         "commerce": false
       },
       "publication": {
-        "release_notes": "3.0.1 updates the LONA listing, logos and starter prompts, and includes review cases for historical backtests and parameter optimization with interactive reports. Tool annotations now explicitly specify all four hints; the report widget loads price candles through lona_get_report_chart."
+        "release_notes": "3.0.2 updates the listing description to cover market data, backtesting, parameter optimization, Day-of-Week and Time-of-Day entry filters, walk-forward analysis, and performance metrics. No MCP connection or runtime changes."
       }
     }
   },