The API result_summary is a flat object with top-level fields
(total_positions, win_rate, profit_factor, net_pnl, sharpe_ratio, etc.)
not a nested backtest_metadata/instruments map. This was causing all
metrics to parse as None/zero for every completed run.
- Rewrite BacktestResult::from_response() to read flat fields directly
- Replace parse_ratio_value/parse_decimal_str with a single parse_number()
that accepts both JSON numbers and decimal strings
- Populate winning_positions, losing_positions, total_fees, avg_bars_in_trade
(previously always None)
- Simplify from_response signature — exchange/base/quote no longer needed
- Add expected_count and coverage_pct to CandleCoverage struct
- Update all example sell rules to use position_quantity instead of "0.01"
- Note that "9999" is a valid sell-all alias (auto-capped by the API)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Upstream added three new quantity sizing objects alongside DecimalString and Expr:
- fixed_sum: buy N quote-currency worth at current price
- percent_of_balance: buy N% of named asset's free balance
- fixed_units: buy exactly N base units (semantic alias for decimal string)
Update dsl-schema.json to include the three definitions and expand
Action.quantity.oneOf to reference all five valid forms.
Update prompts.rs Quantity section to present the declarative methods
as the preferred approach — they're cleaner, more readable, and
instrument-agnostic compared to raw Expr composition.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add /api/v1/strategies/validate client to SwymClient; wire into agent loop
before submission so all DSL errors are surfaced in one round-trip
- Update dsl-schema.json to upstream: quantity is now oneOf[DecimalString, Expr],
ExprApplyFunc uses "input" field (renamed from "expr")
- Update prompts: document expression-based quantity sizing (fixed-fraction and
ATR-based examples), fix apply_func to use "input" not "expr" throughout
- Remove unused ValidationError import
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The previous example quantity "0.001" represented <1% of the $10k
initial balance for BTC and near-zero exposure for ETH/SOL, making
P&L and Sharpe results statistically meaningless.
- Update Quantity section with instrument-appropriate reference values
(BTC: 0.01 ≈ $800, ETH: 3.0 ≈ $600, SOL: 50.0 ≈ $700)
- Replace "0.001" with "0.01" in all four working examples
- Explain that 5–10% of $10k initial balance is the sizing target
- Explicitly warn against "0.001" as it produces negligible exposure
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- bollinger_upper/lower func Exprs must NOT include a "field" parameter;
they compute from close internally. Setting "field":"bollinger_upper"
causes API rejection: expected one of open/high/low/close/volume.
- bollinger Condition "band" only accepts "above_upper" or "below_lower";
"above_lower" and "below_upper" are invalid variants.
Both errors appeared repeatedly across the 50-iteration run, causing
failed backtest submissions on every Bollinger crossover strategy.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add `hma` (Hull MA) and generic `ma` to unsupported func names —
both were used by R1 and rejected by the API
- Note that Hull MA can be approximated via apply_func with wma
- Add `"all"` to the quantity placeholder blacklist; explain that exit
rules must repeat the entry decimal — there is no "close all" concept
Observed in run 2026-03-09T20:10:55: 2 iterations failed on hma/ma,
3 iterations skipped by client-side validation on quantity="all".
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- ADX: clarify it is a FuncName inside {"kind":"func","name":"adx",...},
not a Condition kind — with inline usage example (ADX > 25 filter)
- Expr "kind" field: add explicit note that every Expr object requires
"kind"; {"field":"close"} without "kind" is rejected by the API
- MACD: add Example 4 showing full crossover strategy composed from
bin_op(sub, ema12, ema26) and apply_func(ema,9) as signal line
All three mistakes were observed across consecutive R1-32B runs and
caused repeated API submission failures. Each prompt addition follows
the same pattern as the successful bollinger_upper fix.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Three improvements from the 2026-03-09T18:45:04 run analysis:
**R1 thinking visibility (claude.rs, agent.rs)**
extract_think_content() returns the raw <think> block content before it
is stripped. agent.rs logs it at DEBUG level so 'RUST_LOG=debug' lets
you see why the model keeps repeating a mistake — currently the think
block is silently discarded after stripping.
**Prompt: unsupported indicators and bollinger_upper Expr mistake (prompts.rs)**
- bollinger_upper / bollinger_lower used as {"kind":"bollinger_upper",...}
was the dominant failure in iters 9-15. Added explicit correction:
use {"kind":"func","name":"bollinger_upper","period":20} in Expr context,
never as a standalone kind.
- roc, hma, vwap, macd, cci, stoch are NOT in the swym schema. Added a
clear "NOT supported" list alongside the supported func names.
**Repeated API error detection in diagnose_history (agent.rs)**
If the same "unknown variant `X`" error appears 2+ times in the last 4
iterations, a targeted diagnosis note is emitted naming the bad variant
and pointing to the DSL reference. This surfaces in the next iteration
prompt so the model gets actionable feedback before it wastes another
backtest budget on the same mistake.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Instead of hardcoding per-family token budgets, ClaudeClient queries the
server at startup and sets max_output_tokens = context_length / 2.
Two discovery strategies, tried in order:
1. LM Studio /api/v1/models — returns loaded_instances[].config.context_length
(the actually-configured context, e.g. 64000) and max_context_length
(theoretical max, e.g. 131072). We prefer the loaded value.
2. OpenAI-compat /v1/models/{id} — used as fallback for non-LM Studio
backends that expose context_length on the model object.
If both fail, the family default is kept (DeepSeekR1=32768, Generic=8192).
lmstudio_context_length() matches model IDs with and without quantization
suffixes (@q4_k_m etc.) so the --model flag doesn't need to be exact.
For the current R1-32B setup: loaded context=64000 → max_output_tokens=32000,
giving the thinking pass plenty of room while reserving half for input.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add ModelFamily enum (config.rs) detected from the model name:
- DeepSeekR1: matched on "deepseek-r1", "r1-distill" — R1 thinking blocks
consume thousands of output tokens before the JSON; max_output_tokens
raised to 32768 and HTTP timeout to 300s; prompt tells the model its
<think> output is stripped and only the bare JSON is used
- Generic: previous behaviour (8192 tokens, 120s timeout)
ClaudeClient stores the detected family and uses it for max_tokens and
the request timeout. family() accessor lets the caller (agent.rs) pass
it into system_prompt().
prompts::system_prompt() now accepts &ModelFamily and injects a
family-specific "output format" section in place of the hardcoded
"How to respond" block. New families can be added by extending the
enum and the match arms without touching prompt logic elsewhere.
Also: log full anyhow cause chain (:#) on JSON extraction failure and
show response length alongside the truncated preview, to make future
diagnosis easier.
Root cause of the 2026-03-09T18:29:22 run failure: R1's thinking tokens
counted against max_tokens:8192, leaving only ~500 chars for the actual
JSON, which was always truncated mid-object.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
R1 models use 500-2000 tokens for <think> blocks before the final
response. 4096 was too tight — the model would exhaust the budget
mid-thought and never emit the JSON.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
DeepSeek-R1 models emit <think>...</think> before their actual response.
The brace-counting extractor would grab the first { inside the thinking
block (which contains partial JSON fragments) rather than the final
strategy JSON.
strip_think_blocks() removes all <think>...</think> sections including
unterminated blocks (truncated responses), leaving only the final output
for extract_json to process.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- validate_strategy(): hard error if quantity is not a parseable decimal
(catches "ATR_SIZED" etc. before sending to swym API); soft warning if
a sell rule has no entry_price stop-loss or no bars_since_entry time exit
- Hard validation errors skip the backtest and feed errors back to the LLM
via IterationRecord.validation_notes included in summary()
- json_contains_kind(): recursive helper to search strategy JSON tree
- diagnose_history(): add cycling detection — triggers is_converged when
any avg_sharpe value appears 3+ times in history (not just last 3 streak),
catching the alternating RSI<30 / RSI<25 pattern seen in the latest run
- prompts: clarify that quantity must parse as a float; list invalid
placeholder strings ("ATR_SIZED", "FULL_BALANCE", "dynamic", etc.)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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>
