Add cross-run learning via run ledger and compare endpoint

Persist strategy + run_id to results/run_ledger.jsonl after each backtest.
On startup, load the ledger, fetch metrics via the new compare endpoint
(batched in groups of 50), group by strategy, rank by avg Sharpe, and
inject a summary of the top 5 and worst 3 prior strategies into the
iteration-1 prompt.

Also consumes the enriched result_summary fields from swym patch e47c18:
sortino_ratio, calmar_ratio, max_drawdown, pnl_return, avg_win, avg_loss,
max_win, max_loss, avg_hold_duration_secs. Sortino and max_drawdown are
appended to summary_line() when present.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

src/prompts.rs

@@ -493,9 +493,14 @@ CRITICAL: `apply_func` uses `"input"`, not `"expr"`. Writing `"expr":` will be r
 }
 
 /// Build the user message for the first iteration (no prior results).
-pub fn initial_prompt(instruments: &[String], candle_intervals: &[String]) -> String {
+/// `prior_summary` contains a formatted summary of results from previous runs, if any.
+pub fn initial_prompt(instruments: &[String], candle_intervals: &[String], prior_summary: Option<&str>) -> String {
+    let prior_section = match prior_summary {
+        Some(s) => format!("{s}\n\n"),
+        None => String::new(),
+    };
     format!(
-        r#"Design a trading strategy for crypto spot markets.
+        r#"{prior_section}Design a trading strategy for crypto spot markets.
 
 Available instruments: {}
 Available candle intervals: {}