Three related improvements to help the model learn and explore effectively:
Strategy JSON in history: include the compact strategy JSON in each
IterationRecord::summary() so the LLM knows exactly what was tested in
every past iteration, not just the outcome metrics. Without this the model
had no record of what it tried once conversation history was trimmed.
Rule comment in audit: include rule_comment from the condition audit in
the formatted audit string so the LLM can correlate hit-rate data with
the rule's stated purpose.
Convergence detection and anti-anchoring: diagnose_history() now returns
(String, bool) where the bool signals that the last 3 iterations had
avg_sharpe spread < 0.03 (model stuck in local optimum). When converged:
- Emit a ⚠ CONVERGENCE DETECTED note listing untried candle intervals
- Suppress best_so_far JSON to break the anchoring effect that was
causing the model to produce near-identical strategies for 13+ iterations
- Targeted "try a different approach" instruction
Also add volume-as-field clarification to the DSL mistakes section in
the system prompt, fixing the "unknown variant `volume`" submit error.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The swym API response structure differs from what the code previously
assumed. Fix all field extraction to match the real shape:
- total_positions: backtest_metadata.position_count (not top-level)
- sharpe_ratio, win_rate, profit_factor: instruments.{key}.{field}.value
wrapped decimal strings (not plain floats); treat Decimal::MAX sentinel
(~7.9e28) as None
- net_pnl: instruments.{key}.pnl (plain decimal string)
- instrument key derived as "{exchange_no_underscores}-{base}_{quote}"
Also fix coverage-based backtest_from clamping: after the coverage
check, compute the effective backtest start as the max first_open across
all instruments × common intervals, so strategies never fail with
"requested range outside available data". Log per-interval date ranges
for each instrument at startup.
Additionally:
- Compact format_audit_summary to handle {"rules":[...],"total_bars":N}
structure with per-condition true_count/evaluated breakdown
- Drop avg_bars from summary_line (field absent from API)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The model was generating Expr objects for quantity (e.g. ATR-based sizing),
causing consistent QuantitySpec deserialization failures. Replace the
"prefer dynamic sizing" hint with an explicit rule: quantity must always
be a fixed decimal string like "0.001".
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Shows correct usage of rsi/bollinger/ema_trend condition shortcuts, entry_price
and bars_since_entry ExprKind values, and func/cross_over/bin_op expressions.
Also calls out common model mistakes (rsi as ExprKind, bars_since_entry as
FuncName, expr_field) and adds a note that spot strategies are long-only.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Log full strategy JSON at debug level, show full anyhow cause chain on
submit failures, surface condition_audit_summary for 0-trade results in
both logs and the summary fed back to the AI each iteration.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
