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Glama
fishke22

MARKET_AI_HUB

by fishke22
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
health_checkA

整體健康檢查：models / providers / 環境 / build fingerprint。

get_system_infoB

Python / GPU / CUDA / models（role + task + dual status）/ ensemble / MCP tools / gates / build fingerprint。

get_research_gatesB

Research validation gates：ENGINEERING_GATE / DATA_GATE / MODEL_PREDICTIVE_GATE / TRADING_EDGE_GATE。

每次評估基於 current runtime（singleton 載入），帶 evaluated_at / build_id / evidence。
get_data_source_statusB

各資料來源狀態（TWSE/FinMind/FRED/yfinance/JQuants/TradingView/Broker）。

get_market_dataC

取市場資料（yfinance symbols，或數字代碼走台股流程）。僅回傳結構化資料。

predict_chronosB

Chronos-2 多步預測。horizon "Nd" = N trading bars（1d/2d/5d/10d）。含 build fingerprint。

predict_timesfmC

TimesFM-3.0 多步預測（weights 非商業授權）。含 build fingerprint。

predict_ensembleC

Chronos + TimesFM + XGBoost + LightGBM ensemble。

V1.1 語義：price_ensemble（PRICE_FORECAST 真 quantile）與
direction_ensemble（DIRECTION_CLASSIFICATION）分層；legacy 欄位標 legacy_research_only。
get_model_performanceB

讀取歷史 backtest 績效（model 空白 = 全部）。

分類指標不再硬用 50% 門檻：baseline_threshold = max(majority_class_baseline, uniform_random_baseline)。
沒有資料的欄位回 null（不是 0）。
backtestA

walk-forward backtest（yfinance 資料 + baseline 分類器）。

輸出含分類指標（accuracy/balanced_accuracy/macro_f1/mcc）與 baselines
（uniform_random=1/3、majority_class_baseline、naive direction baseline）。
run_ts_validationB

時間序列模型 rolling-origin OOS 驗證（V1.1 pipeline）。

- 多 rolling origins、no look-ahead、每 fold 預測下一 bar
- 比較 last-price naive / drift / moving-average baselines（MAE/RMSE/MASE）
- interval coverage：p10-p90 nominal 80% 的實測覆蓋率 + calibration error
- deterministic promotion（services/validation.PROMOTION_RULES），結果持久化
analyze_osaka_nikkeiB

大阪日經 PROXY 分析（^N225 index + 跨市場；非 OSE micro 即時）。

requested_dates（可選）："YYYY-MM-DD..YYYY-MM-DD"。^N225 依 TSE 日曆交易；
若窗口含 TSE 休市日（OSE futures 可能 Holiday Trading）→ CALENDAR_TARGET_MISMATCH。
analyze_taiwan_stockA

台股分析（如 2330 / 3706.TW / 華邦電）。只回傳 structured evidence。

get_analysis_packetC

正式分析封包（backend 先完成大部分工作）。

market: osaka | taiwan。target 例：OSE_NIKKEI225_MICRO_FUTURES / 3706.TW。
horizon: 1d/2d/5d/10d。detail_level: compact | normal | audit。
^N225 只能是 PROXY/REFERENCE，不得當 execution target。
get_data_coverageA

大阪微型日經各 factor 資料覆蓋摘要（LIVE_VERIFIED/CONTRACT_ONLY/NEEDS_CONFIG/...）。不得隱藏缺口。

get_event_calendarA

近期重要官方事件日曆（BOJ/Fed/CPI/NFP/PCE/GDP/MOF），只回 Top-N。

get_official_release_snapshotB

官方 macro 來源狀態快照（LIVE_VERIFIED / NEEDS_CONFIG / CONTRACT_ONLY）。

get_target_instrument_stateB

真正交易標的狀態（OSE_NIKKEI225_MICRO_FUTURES）+ Micro settlement + 角色標記。

get_model_leaderboardD

模型 leaderboard（tournament PerformanceStore，含 BEST_BASELINE）。

get_forward_test_statusB

Forward test 註冊狀態（Prediction Registry 中 joint/scenario/ensemble 預測是否已結算）。

get_analysis_archive_statusB

Analysis Archive 狀態（不可變分析 / append-only outcome / reanalysis）。

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

C2.9/5.0

Scored across 21 tools

Disambiguation3/5

Most tools target distinct resource/action pairs, but several status/snapshot tools overlap (health_check vs get_system_info, get_data_source_status vs get_official_release_snapshot vs get_data_coverage), and analyze_taiwan_stock/analyze_osaka_nikkei vs get_analysis_packet have unclear boundaries. The detailed descriptions help, but the tool set is not immediately unambiguous.

Naming Consistency4/5

The dominant patterns get_<noun>, predict_<model>, and analyze_<market> are consistent and readable. Minor deviations like health_check, backtest, and run_ts_validation break the pattern slightly but do not create serious confusion.

Tool Count3/5

21 tools is on the heavy side for an MCP surface, with several status/coverage/snapshot tools that could plausibly be consolidated. The broad hub scope makes the count defensible, but it is borderline and requires agents to absorb a large tool list.

Completeness4/5

The surface covers health, data ingestion, prediction, backtesting, time-series validation, market analysis, and monitoring/archive status. Minor gaps exist around triggering forward tests and retrieving raw prediction outputs, but the core workflows are represented without major dead ends.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources