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Backtesting Arena

by io.github.Schoasch

Suggest a Grid Range

arena_suggest_grid_range

Which price range should my grid bot use? Answers the question arena_run_grid_backtest deliberately leaves open (its own description says: treat range choice as part of the hypothesis). Returns TWO independent range suggestions side by side: iv_anchored (BTCUSDT/ETHUSDT only — sized from option-implied volatility, Deribit DVOL Index; the methodically grounded answer) and recent_volatility (7 trading days of realized volatility before anchor_date — the method exchange auto-modes use, available for all pairs). When both exist and disagree strongly, the options market expects a different volatility regime than the recent past showed — that disagreement is information. Output field names (low_price, high_price, grid_count, grid_type) map 1:1 onto arena_run_grid_backtest inputs. Inputs: pair (required), anchor_date, sigma_mult (IV range width in sigmas, default 1.5), dte (IV horizon in days, default 30). For a historical backtest set anchor_date to your start_date so the volatility range matches the entry, not today; iv_anchored always reflects the latest IV snapshot regardless of anchor_date. Free tier limited to BTCUSDT/ETHUSDT (same gate as the grid backtest itself). [Free tier]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dteNoTarget horizon in days for the IV range. Default 30 (matches the 30d IV tenor).
pairYesCrypto pair symbol, e.g. BTCUSDT. Free tier: BTCUSDT or ETHUSDT only.
sigma_multNoIV range width in sigma multiples. Default 1.5 (covers ~87% of log-normal moves); larger = wider range, fewer range exits, less oscillation capture.
anchor_dateNoYYYY-MM-DD. The realized-volatility range is built from the 7 days BEFORE this date — set it to your backtest start_date. Default: today.

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: -[
-  "pair",
-  "context"
-]New value: +[
+  "pair"
+]
  2. Added

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently states that iv_anchored is only available for BTCUSDT/ETHUSDT, that it always reflects the latest IV snapshot regardless of anchor_date, and that recent_volatility uses 7 days of realized volatility. It also discloses free-tier restrictions and how output fields map to backtest inputs. No side effects are mentioned, but as a suggestion tool, this is sufficient.

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 somewhat long but each sentence contributes value. It front-loads the primary purpose and then details the two methods, input semantics, and usage guidance. While it could be trimmed slightly without losing core info, the structure is logical and the depth is justified for a tool that bridges to a complex backtest workflow.

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?

Despite having no output schema, the description explains the output fields (low_price, high_price, grid_count, grid_type) and their exact mapping to arena_run_grid_backtest inputs. It covers the two independent suggestion methods, parameter behaviors, historical usage, and free-tier constraints. An agent has everything needed to invoke it correctly and interpret the results.

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?

The schema already describes all four parameters with 100% coverage, so the baseline is 3. The description adds meaningful context beyond the schema: it explains that anchor_date should be set to start_date for historical backtests, clarifies that iv_anchored ignores anchor_date, and elaborates on sigma_mult (e.g., default 1.5 covers ~87% of log-normal moves). This enhances parameter understanding beyond the raw schema.

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 clearly states the tool's purpose: to suggest a price range for a grid bot, explicitly answering the question that arena_run_grid_backtest leaves open. It specifies it returns two independent range suggestions (iv_anchored and recent_volatility) and explains their basis, distinguishing it from siblings by naming the alternative tool it complements.

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?

It provides clear guidance on when to use the tool: when you need a range for a grid bot, and importantly, it explains how to set anchor_date for historical backtests (to match the entry date). It also notes free-tier limitations and the pair constraint. It doesn't explicitly say when NOT to use it or name alternative tools beyond the backtest, but the context is strong enough for an agent to decide correctly.

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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