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Binance Futures Alpha Radar

作者 wqyblue3316 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install binance-futures-alpha-radar
功能描述
Analyze Binance USDT-margined perpetual futures symbols with public futures market data only and return a structured market analysis report with a clear BUY,...
使用说明 (SKILL.md)

Binance Futures Strategy Analysis

Overview

Use this skill to produce strategy decisions for Binance U-margined perpetual contracts from public market data only. Do not place orders, do not call any private API, and do not use spot market data.

Workflow

  1. Read the canonical strategy source at ../../../src/main/resources/system-prompt.txt.
  2. Read references/system-prompt-coverage.md before analysis. It mirrors the canonical rules in skill-friendly form and includes the analysis-only output adaptation.
  3. Read ../../../docs/core-feature-spec.md only if you need the project's original execution flow and field semantics.
  4. Read references/analysis-mode.md before deciding, because this skill runs in analysis-only mode instead of live execution mode.
  5. Normalize user input symbols to Binance USDT perpetual symbols:
    • BTC -> BTCUSDT
    • ETH -> ETHUSDT
    • BTCUSDT stays BTCUSDT
  6. Run python3 scripts/binance_futures_snapshot.py \x3CSYMBOL...> with the user symbols.
  7. Use the script output as the only market data source unless the user gives additional account context.
  8. Apply the strategy rules from system-prompt.txt exactly. Do not simplify the decision logic just because this skill is analysis-only.
  9. Build the internal decision object in the canonical schema first, then render it as a human-readable structured report unless the user explicitly asks for raw JSON.

Analysis Mode Rules

  • Default to analysis-only assumptions when the user gives only symbols:
    • equity_usdt = 10000
    • free_usdt = 10000
    • positions = []
    • cooldown_ok = true
  • Replace those defaults if the user provides account equity, free balance, cooldown status, or current positions.
  • Because there is no live position context by default:
    • Use BUY, SELL, or HOLD.
    • Do not emit ADD, REDUCE, or CLOSE unless the user explicitly supplies an existing position.
  • Still fill every required field from the canonical schema:
    • ts
    • instId
    • action
    • position_mode
    • size_usdt
    • order_type
    • stop_loss_px
    • exit_plan
    • reason
    • confidence
    • leverage
    • cooldown_ok
  • Use instId in BTC-USDT style in the internal canonical decision. The script's binance_symbol is only for data fetching.
  • Set order_type to MARKET.
  • Keep reason in simplified Chinese and make it cite actual sequence evidence from the script output.
  • Respect the canonical leverage ladder, confidence buckets, sizing rules, and exit-plan rules even when the final answer is prose.

Default Output Format

Default to a structured Chinese analysis report, not raw JSON.

Include these sections in order unless the user explicitly asks for a different format:

  1. 结论
    • State the final recommended action: BUY / SELL / HOLD.
    • State whether the recommendation is immediate action or wait-for-confirmation.
  2. 趋势判断
    • Explain the 2H strategic direction.
    • Explain the 15m execution structure.
    • Mention 5m only as supporting context.
  3. 关键理由
    • Explain why the chosen action is valid.
    • Explain why the opposite direction is not preferred.
    • Explain why now is or is not the right timing.
  4. 支撑与阻力
    • List near support, key support, near resistance, and key resistance.
    • Distinguish short-term levels from higher-timeframe levels.
  5. 触发条件
    • Explain what price action would upgrade to BUY.
    • Explain what price action would upgrade to SELL.
    • Explain what invalidates the current bias.
  6. 风控与计划
    • Give stop-loss logic, target logic, and management logic in plain Chinese.
    • Mention if current risk/reward is insufficient.

If the user explicitly asks for JSON, return the canonical JSON array instead.

How To Read The Script Output

Focus on these sections:

  • timeframes["2h"]
    • Use for strategic trend class: STRONG_UP, MODERATE_UP, MODERATE_DOWN, STRONG_DOWN.
    • Check price structure, EMA alignment, MACD histogram, RSI, ADX, swing highs/lows, reversal candles, volume anomalies, and volume profile.
  • timeframes["15m"]
    • Use for execution quality.
    • Check breakout confirmation, pullback hold, EMA21 behavior, ATR-based stop distance, recent swing levels, and risk/reward estimates.
  • timeframes["5m"]
    • Use only as short-term context. Do not let it override the 2H strategic filter.
  • funding
    • Use for cost evaluation.
  • depth
    • Use spread and slippage estimates to judge whether execution cost is acceptable.
  • open_interest
    • Use as supporting context, not as a standalone trigger.

Decision Discipline

  • Enforce the 2H directional filter first.
  • Require confirmed 15m structure before any BUY or SELL.
  • Prefer HOLD when confirmation is missing, risk/reward is poor, ATR expansion is too aggressive, or cost is unattractive.
  • For counter-trend setups, allow only the weaker reconnaissance logic that the canonical prompt permits.
  • Always produce an exit_plan with numeric tp and sl, plus a Chinese inv management rule.
  • If the data is incomplete or inconsistent, output HOLD.
  • Internally evaluate all canonical controls:
    • trend class
    • entry confirmation
    • counter-trend exception handling
    • leverage bucket
    • nominal size
    • worst-case risk
    • exit-plan priority
    • cooldown
    • simultaneous-position cap
    • confidence bucket

Coverage Check

This skill is not allowed to rely on a partial summary anymore. Before answering, make sure your reasoning covers all canonical rule groups listed in references/system-prompt-coverage.md.

Script

  • Main helper: scripts/binance_futures_snapshot.py
  • Purpose:
    • Fetch Binance U-margined perpetual public data
    • Compute the indicators and structural helpers required by the strategy
    • Output a compact JSON snapshot for one or more symbols

Output Contract

  • Default:
    • Return a structured Chinese market analysis report.
    • Include a single explicit final recommendation per symbol: BUY, SELL, or HOLD.
    • Include detailed reasons, support and resistance, invalidation conditions, and next-step triggers.
  • If JSON is requested:
    • Return one decision object per requested symbol in a single JSON array.
    • Keep the field order exactly aligned with the canonical prompt.

If the user asks for BTC ETH, analyze both symbols separately in the same response.

安全使用建议
This skill's code appears coherent with its purpose: the Python script fetches public Binance futures data and computes trading indicators, and no credentials are required. However, the SKILL.md explicitly instructs the agent to read files outside the packaged skill (e.g., ../../../src/main/resources/system-prompt.txt), which is unnecessary for public-data analysis and could cause the agent to access sensitive repository content (including internal system prompts). Before installing or enabling this skill, consider: - Confirm whether those external files exist in the environment where the agent will run. If they do not, the instructions may fail; if they do, the skill may read internal files you don't want exposed. - Ask the publisher to bundle any necessary rule files into the skill or remove references to external repo paths. The included references/ files largely mirror the external sources — bundling them would remove the need to read outside files. - Review scripts/binance_futures_snapshot.py locally: it only calls Binance public API (https://fapi.binance.com) and computes indicators; no hidden endpoints or credential exfiltration were found. - Run the skill in a sandboxed environment first, or inspect the referenced external files (system-prompt.txt, core-feature-spec.md) to ensure they contain no sensitive data. If the external file reads are removed or the referenced files are confirmed benign and intentionally included in your runtime environment, this assessment would likely change to benign. Currently treat it as suspicious because of the out-of-bundle file access requirement.
功能分析
Type: OpenClaw Skill Name: binance-futures-alpha-radar Version: 1.0.0 The skill bundle contains instructions in SKILL.md and the references/ directory that direct the AI agent to perform path traversal by reading files outside the skill's scope (e.g., ../../../src/main/resources/system-prompt.txt). While the Python script scripts/binance_futures_snapshot.py is a functional market data fetcher for Binance public APIs, the prompt-based instructions to access arbitrary repository paths are high-risk and could be used to exfiltrate sensitive configuration or source files if the agent's environment is not strictly sandboxed.
能力评估
Purpose & Capability
Name/description align with the code: the included script fetches public Binance USD-M perpetual endpoints and computes indicators. However, SKILL.md requires reading external repo files (../../../src/main/resources/system-prompt.txt and ../../../docs/core-feature-spec.md) that are not part of the skill bundle. Those external reads are not required to fetch public market data and are disproportionate to the stated purpose.
Instruction Scope
Runtime instructions tell the agent to read several files outside the skill (canonical system-prompt and core docs) and to apply those rules exactly. This grants the skill discretion to access arbitrary repository content on the host environment. The script itself only calls Binance public APIs (BASE_URL = https://fapi.binance.com) and does not exfiltrate data, but the instruction to open ../../../src/... files is scope creep and a potential data-leak vector.
Install Mechanism
No install spec; instruction-only plus a local Python script. This is low-risk from an install standpoint since nothing is downloaded or written at install.
Credentials
The skill requests no environment variables, credentials, or config paths. The included Python script uses only public Binance endpoints and standard libraries. No secrets are requested or required.
Persistence & Privilege
always is false and the skill does not request persistent/system-wide privileges. Autonomous invocation is allowed by default (normal). The skill does not modify other skills or system-wide config.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install binance-futures-alpha-radar
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /binance-futures-alpha-radar 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release of Binance Futures Strategy Analysis (v1.0.0): - Provides structured market analysis reports for Binance USDT-margined perpetual contracts using only public futures market data. - Gives clear BUY, SELL, or HOLD recommendations without executing trades or accessing private APIs. - Normalizes user symbols and integrates directly with a snapshot script for real-time data. - Follows detailed system-prompt trading rules for decision logic, risk, and output format. - Defaults to Chinese structured reports but supports canonical JSON output if requested. - Adapts recommendations based on user-provided or default account context.
元数据
Slug binance-futures-alpha-radar
版本 1.0.0
许可证 MIT-0
累计安装 1
当前安装数 1
历史版本数 1
常见问题

Binance Futures Alpha Radar 是什么?

Analyze Binance USDT-margined perpetual futures symbols with public futures market data only and return a structured market analysis report with a clear BUY,... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 454 次。

如何安装 Binance Futures Alpha Radar?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install binance-futures-alpha-radar」即可一键安装,无需额外配置。

Binance Futures Alpha Radar 是免费的吗?

是的,Binance Futures Alpha Radar 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Binance Futures Alpha Radar 支持哪些平台?

Binance Futures Alpha Radar 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Binance Futures Alpha Radar?

由 wqyblue3316(@wqyblue3316)开发并维护,当前版本 v1.0.0。

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