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Backtesting Arena

by io.github.Schoasch

Get Strategy Performance Snapshot (per Asset)

arena_get_strategy_performance

How did this exact strategy, asset and interval perform? Aggregated backtest performance for ONE specific (strategy, asset, interval) combination. Returns run_count, avg_cagr, avg_win_rate, avg_drawdown, effective_years, vs_buy_hold comparison (beats_buy_hold, cagr_delta) and an evidence block declaring the gate machine-readably (gate_applies_to: stats.run_count, threshold 5 runs, benchmark value, aggregation data window). For multi-strategy overview use arena_get_strategy_insights. Use this to answer 'How does strategy X perform on asset Y?'. [Free tier]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
assetYesCrypto pair / symbol (e.g. BTCUSDT, ETHUSDT). Case-insensitive.
intervalNoDefault '1w'. 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.
strategyYesStrategy key (e.g. rsi_sma, golden_cross). See arena_get_strategies for valid keys.
asset_typeNoOptional asset class filter to disambiguate (e.g. when same pair-name exists in two classes).
ref_strategyNoBenchmark reference. Default 'bh' (Buy & Hold).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 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"
-}
    • changedInput schema / required
      Previous value: -[
-  "strategy",
-  "asset",
-  "context"
-]New value: +[
+  "strategy",
+  "asset"
+]
  2. Changed1 schema field changed
    • changedInput schema / properties / interval / description
      Previous value: -"Default '1w'."New value: +"Default '1w'. 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."
  3. Changed1 schema field changed
    • changedInput schema / properties / interval / enum
      Previous value: -[
-  "1d",
-  "1w",
-  "1M"
-]New value: +[
+  "1d",
+  "2d",
+  "3d",
+  "1w",
+  "1M"
+]
  4. Changed1 schema field changed
    • changedInput schema / properties / asset_type / enum
      Previous value: -[
-  "crypto"
-]New value: +[
+  "crypto",
+  "tokenized_equity",
+  "tokenized_etf",
+  "commodities"
+]
  5. Changed2 schema fields changed
    • changedInput schema / properties / asset / description
      Previous value: -"Pair / symbol (e.g. BTCUSDT, AAPL.US). Case-insensitive."New value: +"Crypto pair / symbol (e.g. BTCUSDT, ETHUSDT). Case-insensitive."
    • changedInput schema / properties / asset_type / enum
      Previous value: -[
-  "crypto",
-  "stock",
-  "etf",
-  "commodities",
-  "forex"
-]New value: +[
+  "crypto"
+]
  6. First observed

TDQS

A4.8/5.0
Behavior5/5

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

No annotations exist, so the description carries the full burden. It discloses not just the return fields but also the machine-readable `evidence` block, the gate threshold (5 runs), and a critical behavioral nuance: the interval alignment issue for 2d/3d candles and its measurable impact on CAGR (6.66 pp on average). This is rich, honest behavioral disclosure beyond any annotation.

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?

The description is dense but every sentence earns its place: the opening question orients the agent, the return list is compact, the sibling pointer is explicit, and the interval caveat is crucial. It is longer than the two-sentence gold standard but not bloated; a 4 reflects the tight packing rather than fluff.

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?

For a 5-parameter tool with no output schema, the description provides a complete picture: it names the output fields and their sematics (including the evidence block with gate_applies_to, threshold, benchmark, aggregation window), states the defaults, and covers the alignment caveat that could otherwise lead to misinterpretation. An agent has everything needed to invoke and interpret results correctly.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds value by specifying defaults for `interval` and `ref_strategy` (not in the schema) and by explaining the alignment-dependent behavior of multi-day intervals — semantic nuance that materially affects result interpretation. It stops short of fully explaining `asset_type` disambiguation beyond what the schema already says, hence 4 rather than 5.

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

Purpose5/5

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

The description states a specific verb ('Get'), a precise resource ('strategy performance snapshot per asset'), and clearly delimits scope ('ONE specific (strategy, asset, interval) combination'). It lists the exact returned fields and explicitly distinguishes this from the multi-strategy sibling `arena_get_strategy_insights`. No ambiguity about what the tool does.

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

Usage Guidelines5/5

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

It gives an explicit when-to-use ('Use this to answer ...') and names the alternative for multi-strategy overview (`arena_get_strategy_insights`). This is enough for an agent to choose correctly, and the contrast with the sibling is clear.

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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TDQS

A3.6/5.0
Disambiguation2/5

Many tools cover overlapping market indicators (e.g., cycle state, pulse, bullmarket ampel, volatility phases) and several share similar get_*_history patterns, which could cause an agent to select the wrong one. However, each tool has detailed descriptions with explicit references to related tools to reduce ambiguity.

Naming Consistency3/5

Tool names generally follow a verb_noun pattern (arena_get_*, arena_list_*, arena_run_*, arena_subscribe_*), but there are inconsistencies such as 'validate_strategy' (no arena_ prefix), 'arena_status' (not a clear verb_noun), and variations like 'arena_run_grid_backtest' vs 'arena_run_backtest'.

Tool Count2/5

With 84 tools, the server is heavily over-scoped for a typical MCP server, likely causing navigation and selection overhead. While the domain (crypto backtesting and analytics) is broad, 84 tools exceed reasonable coherence and impose a high cognitive load.

Completeness4/5

The tool surface covers a wide range of analytics (market indicators, backtesting, validation, alerts, subscriptions, reports) with few obvious dead ends. Minor gaps exist like lack of direct portfolio management or strategy editing, but core workflows are well covered.

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