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Backtesting Arena

by io.github.Schoasch
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Get Strategy Insights Matrix or Detail

arena_get_strategy_insights
Read-only

Which strategy and interval combinations actually performed? Aggregated backtest performance per (strategy × interval) cell. If strategy AND interval provided, returns detail with per-asset breakdown + param variants. Otherwise returns the matrix. Free tier is limited to the same strategies that are free in the backtester itself (rsi_sma, golden_cross, rsi_ob_os, bnh_fixed, dca_reference, dca_reference_v2); the response then carries plan_capped: true plus plan_cap_note, so a short matrix is never mistaken for a thin database. Detail mode on a Pro-only strategy returns 403 rather than a silently empty answer. API Pro and Power receive every cell. Counts: runCount = deduplicated runs above the trade floor that carry the averages, inertRuns = 0-trade runs of the same cell counted IN ADDITION, runs_total = both. avgWinRate averages only runs with a rated trade (0-trade runs and open single positions store 0, which is not a hit rate); avgBuyholdCagr/beatsBuyhold are shown only when >= 50 % of the cell's assets carry the strategy-window benchmark (bhAssetCoverage) — the envelope carries benchmark_definition, win_rate_definition and counts_definition. Matrix mode also carries summary (cells_total, cells_with_benchmark, cells_beating_bh, cells_beating_bh_with_negative_cagr, cells_without_cagr) counted over the cells actually returned — quote those instead of counting cells yourself. [Free: 6 strategies / Pro+: full]

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "arena_get_strategy_insights"
  }
}

Input Schema

NameRequiredDescriptionDefault
intervalNoDetail mode: interval. Candle interval: '1d' daily, '2d'/'3d' multi-day, '1w' weekly, '1M' monthly. Multi-day candles (2d/3d) are anchored to the Unix epoch, so one of n possible alignments is used. Measured on our own corpus, the choice of alignment alone moves CAGR by 6.66 pp on average (max 12.30). Treat differences below that as not distinguishable — 1d/2d/3d behaved as one block in our tests, not a ranking.
min_runsNoMatrix mode: minimum runs per cell. Default 5.
strategyNoDetail mode: strategy key (used together with `interval`).
asset_typeNoRestrict to one asset class.
assets_modeNo'top10' restricts to top-10 pairs by run-count.
ref_strategyNoBenchmark reference. Default 'bh'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / min_runs / maximum
      Added value: +9007199254740991
  2. Changed3 schema fields changed
    • addedInput schema / additionalProperties
      Added value: +false
    • removedInput schema / properties / context
      Removed value: -{
-  "description": "Explain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): \"Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization.\"",
-  "type": "string"
-}
    • removedInput schema / required
      Removed value: -[
-  "context"
-]
  3. Changed1 schema field changed
    • changedInput schema / properties / interval / description
      Previous value: -"Detail mode: interval."New value: +"Detail mode: interval. Candle interval: '1d' daily, '2d'/'3d' multi-day, '1w' weekly, '1M' monthly. Multi-day candles (2d/3d) are anchored to the Unix epoch, so one of n possible alignments is used. Measured on our own corpus, the choice of alignment alone moves CAGR by 6.66 pp on average (max 12.30). Treat differences below that as not distinguishable — 1d/2d/3d behaved as one block in our tests, not a ranking."
  4. Changed1 schema field changed
    • changedInput schema / properties / interval / enum
      Previous value: -[
-  "1d",
-  "1w",
-  "1M"
-]New value: +[
+  "1d",
+  "2d",
+  "3d",
+  "1w",
+  "1M"
+]
  5. Changed1 schema field changed
    • changedInput schema / properties / asset_type / enum
      Previous value: -[
-  "crypto"
-]New value: +[
+  "crypto",
+  "tokenized_equity",
+  "tokenized_etf",
+  "commodities"
+]
  6. Changed1 schema field changed
    • changedInput schema / properties / asset_type / enum
      Previous value: -[
-  "crypto",
-  "stock",
-  "etf",
-  "commodities",
-  "forex"
-]New value: +[
+  "crypto"
+]
  7. First observed

TDQS

A4.5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With readOnlyHint already covering safety, the description goes well beyond: plan_capped/plan_cap_note flags so a short matrix isn't mistaken for thin data, 403 on Pro-only detail mode instead of a silent empty answer, and exact definitions of runCount/inertRuns/runs_total/avgWinRate. It even tells the agent to quote the envelope's summary counts rather than counting cells itself.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose and mode rule are front-loaded, then the plan-cap and statistical caveats. Dense but nearly every clause carries load. The count-definition and benchmark-coverage sentences are packed tightly enough that they read as a wall, costing some skimmability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description takes on full responsibility for return shape and does so: matrix vs detail payloads, per-asset breakdown, param variants, plan_capped fields, benchmark_definition/win_rate_definition/counts_definition, and the summary block. Nothing material is missing for a correct call.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds the crucial cross-parameter interaction (strategy + interval together switch to detail mode) that the schema does not encode per-property, plus the >=50% bhAssetCoverage threshold governing when avgBuyholdCagr appears. This is meaning the agent cannot derive from the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Opens with a specific question the tool answers and names the resource precisely: aggregated backtest performance per (strategy × interval) cell, with two distinct modes. An agent immediately knows what it gets. It stops short of naming the nearest sibling (arena_get_strategy_performance) to disambiguate against it.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states the mode-selection rule (strategy AND interval → detail, otherwise matrix) and the free-tier constraint with the named strategies. It doesn't tell the agent when to prefer this over arena_get_strategy_performance or arena_get_strategy_performance_by_regime, so the sibling routing gap remains.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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