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Browse Robonet Data

浏览Robonet数据

Quick Start

快速入门

This skill provides fast, read-only access to explore Robonet's trading resources before building anything. All tools execute in under 1 second and cost little to nothing.

Load the tools first:

Use MCPSearch to select: mcp__workbench__get_all_symbols
Use MCPSearch to select: mcp__workbench__get_all_technical_indicators
Use MCPSearch to select: mcp__workbench__get_data_availability

Common starting pattern:

1. get_all_symbols → See available trading pairs (BTC-USDT, ETH-USDT, etc.)
2. get_all_technical_indicators → Browse 170+ indicators (RSI, MACD, Bollinger Bands)
3. get_data_availability → Check data ranges before backtesting

When to use this skill:

Start every workflow by exploring available resources
Check data availability before building strategies
Review existing strategies and their performance
Understand what ML predictions are available (Allora topics)
Audit recent backtest results

此技能提供快速的只读访问权限，可在构建任何内容前探索Robonet的交易资源。所有工具执行时间不到1秒，成本极低甚至免费。

先加载工具:

Use MCPSearch to select: mcp__workbench__get_all_symbols
Use MCPSearch to select: mcp__workbench__get_all_technical_indicators
Use MCPSearch to select: mcp__workbench__get_data_availability

常用起始流程:

1. get_all_symbols → 查看可用交易对（BTC-USDT、ETH-USDT等）
2. get_all_technical_indicators → 浏览170+指标（RSI、MACD、布林带）
3. get_data_availability → 回测前检查数据范围

何时使用此技能:

在每个工作流开始时探索可用资源
构建策略前检查数据可用性
查看现有策略及其表现
了解可用的ML预测（Allora主题）
审核近期回测结果

Available Tools (8)

可用工具（8个）

Strategy Exploration Tools

策略探索工具

get_all_strategies
- List your trading strategies with optional backtest results

Parameters:
- ```
include_latest_backtest
```
  (optional, boolean): Include latest backtest summaries
Returns: List of strategies with names, components, and optionally performance metrics
Pricing: $0.001
Use when: Finding existing strategies to review, enhance, or compare

get_strategy_code
- View Python source code of a strategy

Parameters:
- ```
strategy_name
```
  (required, string): Name of the strategy
Returns: Complete Python source code
Pricing: Free
Use when: Learning from existing strategies, reviewing before modification, debugging

get_strategy_versions
- Track strategy evolution across versions

Parameters:
- ```
base_strategy_name
```
  (required, string): Base name without version suffixes
Returns: List of all versions with creation dates and modification history
Pricing: $0.001
Use when: Understanding how a strategy evolved, comparing versions, auditing changes

get_all_strategies
- 列出你的交易策略，可选择包含回测结果

参数:
- ```
include_latest_backtest
```
  （可选，布尔值）：包含最新回测摘要
返回值: 包含策略名称、组件及可选表现指标的策略列表
定价: 0.001美元
适用场景: 查找现有策略进行查看、优化或对比

get_strategy_code
- 查看策略的Python源代码

参数:
- ```
strategy_name
```
  （必填，字符串）：策略名称
返回值: 完整的Python源代码
定价: 免费
适用场景: 学习现有策略、修改前查看、调试

get_strategy_versions
- 跟踪策略的版本演变

参数:
- ```
base_strategy_name
```
  （必填，字符串）：不带版本后缀的基础策略名称
返回值: 包含所有版本的创建日期和修改历史的列表
定价: 0.001美元
适用场景: 了解策略的演变过程、对比版本、审核变更

Market Data Tools

市场数据工具

get_all_symbols
- List tradeable pairs on Hyperliquid Perpetual

Parameters:
- ```
exchange
```
  (optional, string): Filter by exchange name
- ```
active_only
```
  (optional, boolean): Only return active symbols (default: true)
Returns: List of symbols with exchange, symbol name, active status, backfill status
Pricing: $0.001
Use when: Choosing which assets to trade, checking what's available before building strategies

get_data_availability
- Check data ranges before backtesting

Parameters:
- ```
data_type
```
  (optional, string): Type of data (crypto, polymarket, all)
- ```
symbols
```
  (optional, array): Specific crypto symbols to check
- ```
exchange
```
  (optional, string): Filter crypto by exchange
- ```
asset
```
  (optional, string): Filter Polymarket by asset
- ```
include_resolved
```
  (optional, boolean): Include resolved Polymarket markets
- ```
only_with_data
```
  (optional, boolean): Only show items with available data
Returns: Data availability with date ranges, candle counts, backfill status
Pricing: $0.001
Use when: Before backtesting (verify sufficient data), choosing test date ranges, checking market coverage

get_all_symbols
- 列出Hyperliquid Perpetual上的可交易对

参数:
- ```
exchange
```
  （可选，字符串）：按交易所名称过滤
- ```
active_only
```
  （可选，布尔值）：仅返回活跃交易对（默认：true）
返回值: 包含交易所、交易对名称、活跃状态、补全数据状态的交易对列表
定价: 0.001美元
适用场景: 选择要交易的资产、构建策略前查看可用资源

get_data_availability
- 回测前检查数据范围

参数:
- ```
data_type
```
  （可选，字符串）：数据类型（crypto、polymarket、all）
- ```
symbols
```
  （可选，数组）：要检查的特定加密货币交易对
- ```
exchange
```
  （可选，字符串）：按交易所过滤加密货币数据
- ```
asset
```
  （可选，字符串）：按资产过滤Polymarket数据
- ```
include_resolved
```
  （可选，布尔值）：包含已结算的Polymarket市场
- ```
only_with_data
```
  （可选，布尔值）：仅显示有可用数据的项目
返回值: 包含日期范围、K线数量、补全数据状态的数据可用性信息
定价: 0.001美元
适用场景: 回测前（验证数据是否充足）、选择测试日期范围、检查市场覆盖情况

Indicator & ML Tools

指标与ML工具

get_all_technical_indicators
- Browse 170+ indicators available in Jesse framework

Parameters:
- ```
category
```
  (optional, string): Filter by category (momentum, trend, volatility, volume, overlap, oscillators, cycle, all)
Returns: List of indicators with names, categories, and parameters
Pricing: $0.001
Use when: Exploring indicators for strategy ideas, checking parameter requirements, learning what's available

get_allora_topics
- List Allora Network ML prediction topics

Parameters: None
Returns: List of topics with asset names, network IDs, prediction horizons, and prediction types
Pricing: $0.001
Use when: Planning ML enhancement, checking prediction coverage, understanding available horizons (5m, 8h, 24h, 1 week)

get_all_technical_indicators
- 浏览Jesse框架中可用的170+指标

参数:
- ```
category
```
  （可选，字符串）：按类别过滤（momentum、trend、volatility、volume、overlap、oscillators、cycle、all）
返回值: 包含指标名称、类别及参数的指标列表
定价: 0.001美元
适用场景: 为策略思路探索指标、检查参数要求、了解可用资源

get_allora_topics
- 列出Allora Network的ML预测主题

参数: 无
返回值: 包含资产名称、网络ID、预测周期、预测类型的主题列表
定价: 0.001美元
适用场景: 规划ML优化、检查预测覆盖范围、了解可用周期（5分钟、8小时、24小时、1周）

Backtest Results Tool

回测结果工具

get_latest_backtest_results
- View recent backtest performance

Parameters:
- ```
strategy_name
```
  (optional, string): Filter by strategy name
- ```
limit
```
  (optional, integer, 1-100): Number of results (default: 10)
- ```
include_equity_curve
```
  (optional, boolean): Include equity curve timeseries
- ```
equity_curve_max_points
```
  (optional, integer, 50-1000): Maximum points for equity curve
Returns: List of backtest records with metrics (Sharpe, drawdown, win rate, total return, profit factor)
Pricing: Free
Use when: Checking if backtest already exists, comparing strategy performance, avoiding redundant backtests

get_latest_backtest_results
- 查看近期回测表现

参数:
- ```
strategy_name
```
  （可选，字符串）：按策略名称过滤
- ```
limit
```
  （可选，整数，1-100）：结果数量（默认：10）
- ```
include_equity_curve
```
  （可选，布尔值）：包含权益曲线时间序列
- ```
equity_curve_max_points
```
  （可选，整数，50-1000）：权益曲线的最大点数
返回值: 包含指标（夏普比率、最大回撤、胜率、总收益、利润因子）的回测记录列表
定价: 免费
适用场景: 检查回测是否已存在、对比策略表现、避免重复回测

Core Concepts

核心概念

Symbol Coverage

交易对覆盖范围

Crypto Perpetuals (Hyperliquid):

Major pairs: BTC-USDT, ETH-USDT, SOL-USDT, NEAR-USDT
Data history: BTC-USDT and ETH-USDT have longest history (2020-present)
Typical range: Most symbols have 6-24 months of data
Data quality: 1-minute candles available for all symbols

Best practices:

Use
```
get_all_symbols
```
to see complete list
Check
```
get_data_availability
```
for specific symbol history
BTC-USDT and ETH-USDT recommended for initial strategy development (longest history)

加密货币永续合约（Hyperliquid）:

主流交易对: BTC-USDT、ETH-USDT、SOL-USDT、NEAR-USDT
数据历史: BTC-USDT和ETH-USDT的历史数据最久（2020年至今）
典型范围: 大多数交易对拥有6-24个月的数据
数据质量: 所有交易对均提供1分钟K线数据

最佳实践:

使用
```
get_all_symbols
```
查看完整列表
使用
```
get_data_availability
```
检查特定交易对的历史数据
初始策略开发推荐使用BTC-USDT和ETH-USDT（历史数据最久）

Technical Indicators

技术指标

170+ indicators organized by category:

Momentum (16 indicators): RSI, MACD, Stochastic, ADX, CCI, MFI, ROC, Williams %R, Ultimate Oscillator, etc.
Trend (12 indicators): EMA, SMA, DEMA, TEMA, WMA, Supertrend, Parabolic SAR, VWAP, HMA, etc.
Volatility (8 indicators): Bollinger Bands, ATR, Keltner Channels, Donchian Channels, Standard Deviation, etc.
Volume (10 indicators): OBV, Volume Profile, Chaikin Money Flow, Volume Weighted indicators, etc.
Overlap (8 indicators): Various moving averages and envelopes
Oscillators (6 indicators): Specialized momentum oscillators
Cycle (4 indicators): Market cycle detection indicators

How to use:

1. get_all_technical_indicators(category="momentum") → Browse momentum indicators
2. Pick indicators for your strategy concept
3. Reference indicators in strategy description when building

Note: All indicators are from the Jesse framework (

jesse.indicators

). Use exact names when creating strategies.

170+指标按类别划分:

动量类（16个指标）: RSI、MACD、Stochastic、ADX、CCI、MFI、ROC、Williams %R、Ultimate Oscillator等
趋势类（12个指标）: EMA、SMA、DEMA、TEMA、WMA、Supertrend、Parabolic SAR、VWAP、HMA等
波动率类（8个指标）: 布林带、ATR、Keltner Channels、Donchian Channels、标准差等
成交量类（10个指标）: OBV、成交量分布、Chaikin Money Flow、成交量加权指标等
重叠类（8个指标）: 各种移动平均线和包络线
震荡类（6个指标）: 专业动量震荡指标
周期类（4个指标）: 市场周期检测指标

使用方法:

1. get_all_technical_indicators(category="momentum") → 浏览动量类指标
2. 为你的策略思路选择指标
3. 构建策略时在描述中引用指标

注意: 所有指标均来自Jesse框架（

jesse.indicators

）。创建策略时请使用精确名称。

Allora Network ML Predictions

Allora Network ML预测

Prediction Coverage:

Assets: BTC, ETH, SOL, NEAR
Horizons: 5 minutes, 8 hours, 24 hours, 1 week
Prediction types:
- Log return (percentage change prediction)
- Absolute price (future price prediction)
Networks:
- Mainnet: 10 production topics
- Testnet: 26 experimental topics

Topic structure:

Asset: BTC
Horizon: 24h
Type: Log return
Network: mainnet

How to use:

1. get_allora_topics() → See all available predictions
2. Match prediction horizon to your strategy timeframe
3. Use enhance_with_allora (from improve-trading-strategies skill) to integrate predictions

Best practices:

Match prediction horizon to strategy timeframe (don't use 5m predictions for daily strategy)
Mainnet topics are production-ready, testnet topics are experimental
Check topic availability before planning ML enhancement

预测覆盖范围:

资产: BTC、ETH、SOL、NEAR
周期: 5分钟、8小时、24小时、1周
预测类型:
- 对数收益率（百分比变化预测）
- 绝对价格（未来价格预测）
网络:
- 主网: 10个生产环境主题
- 测试网: 26个实验性主题

主题结构:

Asset: BTC
Horizon: 24h
Type: Log return
Network: mainnet

使用方法:

1. get_allora_topics() → 查看所有可用预测
2. 将预测周期与你的策略时间框架匹配
3. 使用enhance_with_allora（来自improve-trading-strategies技能）集成预测

最佳实践:

预测周期需与策略时间框架匹配（不要将5分钟预测用于日线策略）
主网主题可用于生产环境，测试网主题为实验性
规划ML优化前请检查主题可用性

Backtest Result Interpretation

回测结果解读

Key Metrics:

Sharpe Ratio (risk-adjusted return):

>2.0: Excellent performance
1.0-2.0: Good performance
0.5-1.0: Acceptable performance
<0.5: Poor performance

Max Drawdown (largest peak-to-trough decline):

<10%: Conservative risk profile
10-20%: Moderate risk profile
20-40%: Aggressive risk profile
>40%: Very risky (reconsider strategy)

Win Rate (percentage of profitable trades):

45-65%: Realistic for most strategies
>70%: Suspicious (possible overfitting or unrealistic fills)
<40%: Needs improvement

Profit Factor (gross profit / gross loss):

>2.0: Excellent
1.5-2.0: Good
1.2-1.5: Acceptable
<1.2: Marginal (risky to deploy)

How to use backtest results:

1. get_latest_backtest_results(strategy_name="MyStrategy") → Check recent tests
2. Review metrics against benchmarks above
3. If metrics good: consider deployment
4. If metrics poor: refine strategy or try different approach

关键指标:

夏普比率（风险调整后收益）:

>2.0: 表现极佳
1.0-2.0: 表现良好
0.5-1.0: 表现可接受
<0.5: 表现较差

最大回撤（最大峰谷跌幅）:

<10%: 保守风险配置
10-20%: 中等风险配置
20-40%: 激进风险配置
>40%: 风险极高（重新考虑策略）

胜率（盈利交易占比）:

45-65%: 大多数策略的合理范围
>70%: 可疑（可能存在过拟合或不现实的成交假设）
<40%: 需要改进

利润因子（总盈利/总亏损）:

>2.0: 极佳
1.5-2.0: 良好
1.2-1.5: 可接受
<1.2: 边际收益（部署风险高）

回测结果使用方法:

1. get_latest_backtest_results(strategy_name="MyStrategy") → 查看近期测试结果
2. 将指标与上述基准对比
3. 若指标良好：考虑部署
4. 若指标较差：优化策略或尝试其他方法

Best Practices

最佳实践

Exploration Workflow

探索工作流

Start every strategy development with data exploration:

1. Explore available assets
   get_all_symbols() → What can I trade?
   get_data_availability(data_type="crypto") → How much history?

2. Understand available tools
   get_all_technical_indicators(category="momentum") → What indicators?
   get_allora_topics() → What ML predictions available?

3. Review existing work
   get_all_strategies(include_latest_backtest=true) → What's already built?
   get_strategy_code(strategy_name="Existing") → Learn from existing code

4. Plan your strategy
   → Use insights from exploration to inform strategy design

策略开发前先进行数据探索:

1. 探索可用资产
   get_all_symbols() → 我可以交易什么？
   get_data_availability(data_type="crypto") → 有多久的历史数据？

2. 了解可用工具
   get_all_technical_indicators(category="momentum") → 有哪些指标？
   get_allora_topics() → 有哪些可用的ML预测？

3. 查看现有成果
   get_all_strategies(include_latest_backtest=true) → 已经构建了哪些策略？
   get_strategy_code(strategy_name="Existing") → 从现有代码中学习

4. 规划你的策略
   → 利用探索所得的见解指导策略设计

Data Availability Checks

数据可用性检查

Always verify sufficient data before backtesting:

Problem: Backtest fails with "No data available"
Solution:
  1. get_data_availability(symbols=["BTC-USDT"], only_with_data=true)
  2. Check date range returned
  3. Use date range within available data for backtest

Minimum data requirements:

Quick test: 1-3 months (limited validation)
Standard test: 6-12 months (recommended minimum)
Robust test: 12-24 months (ideal for validation)

回测前务必验证数据是否充足:

问题：回测因“无可用数据”失败
解决方案:
  1. get_data_availability(symbols=["BTC-USDT"], only_with_data=true)
  2. 查看返回的日期范围
  3. 在可用数据范围内选择回测的日期范围

最低数据要求:

快速测试: 1-3个月（验证有限）
标准测试: 6-12个月（推荐最低要求）
稳健测试: 12-24个月（理想验证周期）

Cost Optimization

成本优化

All tools in this skill are cheap (free to $0.001):

Use liberally during exploration
No need to batch queries or optimize calls
Better to over-explore than under-explore

Cost-saving pattern:

1. Browse data (this skill, <$0.01) → Explore thoroughly
2. Generate ideas (design-trading-strategies, $0.05-$1.00) → Cheap exploration
3. Build strategy (build-trading-strategies, $1-$4.50) → Expensive, be sure first

Spending 2-3 minutes exploring data (costs <$0.01) can save dollars in wasted strategy generation.

此技能中的所有工具成本极低（免费至0.001美元）:

探索阶段可随意使用
无需批量查询或优化调用
过度探索比探索不足更好

成本节省流程:

1. 浏览数据（此技能，<0.01美元）→ 充分探索
2. 生成思路（design-trading-strategies，0.05-1.00美元）→ 低成本探索
3. 构建策略（build-trading-strategies，1-4.50美元）→ 成本较高，需先确认需求

花2-3分钟探索数据（成本<0.01美元）可节省因策略生成失误产生的数美元成本。

Learning from Existing Strategies

从现有策略中学习

Use existing strategies as templates:

1. get_all_strategies(include_latest_backtest=true)
   → Find high-performing strategies (Sharpe >1.5)

2. get_strategy_code(strategy_name="HighPerformer")
   → Study the code structure

3. Identify patterns:
   - How are entry conditions structured?
   - What indicators are used?
   - How is position sizing calculated?
   - How is risk management implemented?

4. Apply learnings to new strategy design

将现有策略作为模板:

1. get_all_strategies(include_latest_backtest=true)
   → 寻找高表现策略（夏普比率>1.5）

2. get_strategy_code(strategy_name="TopPerformer")
   → 研究代码结构

3. 识别模式:
   - 入场条件如何构建？
   - 使用了哪些指标？
   - 仓位大小如何计算？
   - 风险管理如何实现？

4. 将所学应用到新策略设计中

Indicator Research

指标研究

Find the right indicators for your strategy concept:

Strategy Type → Indicator Categories to explore:
- Trend Following → trend, momentum
- Mean Reversion → oscillators, momentum
- Breakout → volatility, volume
- Scalping → momentum, volume
- Swing Trading → trend, overlap

Example exploration:

Building a mean reversion strategy:
1. get_all_technical_indicators(category="oscillators") → See oscillators
2. get_all_technical_indicators(category="momentum") → See momentum indicators
3. Pick RSI (overbought/oversold) + Bollinger Bands (deviation from mean)
4. Use these indicator names when building strategy

为你的策略概念选择合适的指标:

策略类型 → 要探索的指标类别:
- 趋势跟踪 → 趋势类、动量类
- 均值回归 → 震荡类、动量类
- 突破 → 波动率类、成交量类
- 高频交易 → 动量类、成交量类
- 波段交易 → 趋势类、重叠类

探索示例:

构建加密货币均值回归策略:
1. get_all_technical_indicators(category="oscillators") → 查看震荡类指标
2. get_all_technical_indicators(category="momentum") → 查看动量类指标
3. 选择RSI（识别超买/超卖）+ 布林带（衡量偏离均值的程度）
4. 构建策略时使用这些指标的精确名称

Common Workflows

常用工作流

Workflow 1: Pre-Strategy Exploration

工作流1：策略构建前探索

Goal: Understand what's available before building anything

1. get_all_symbols()
   → Review available trading pairs
   → Note which symbols interest you

2. get_data_availability(symbols=["BTC-USDT", "ETH-USDT"], only_with_data=true)
   → Check data ranges for chosen symbols
   → Verify sufficient history (6+ months preferred)

3. get_all_technical_indicators(category="all")
   → Browse all 170+ indicators
   → Note which indicators fit your strategy idea

4. get_allora_topics()
   → See ML prediction coverage
   → Check if your asset has predictions available
   → Note prediction horizons

5. Ready to build:
   → If exploring ideas: Use design-trading-strategies skill
   → If ready to code: Use build-trading-strategies skill

Cost: ~$0.005 (essentially free)

目标: 构建任何内容前了解可用资源

1. get_all_symbols()
   → 查看可用交易对
   → 记录你感兴趣的交易对

2. get_data_availability(symbols=["BTC-USDT", "ETH-USDT"], only_with_data=true)
   → 检查所选交易对的日期范围
   → 验证是否有充足的历史数据（推荐6个月以上）

3. get_all_technical_indicators(category="all")
   → 浏览所有170+指标
   → 记录符合你策略思路的指标

4. get_allora_topics()
   → 查看ML预测覆盖范围
   → 检查你的资产是否有可用预测
   → 记录预测周期

5. 准备构建:
   → 若要探索思路：使用design-trading-strategies技能
   → 若要编写代码：使用build-trading-strategies技能

成本: 约0.005美元（几乎免费）

Workflow 2: Strategy Audit

工作流2：策略审核

Goal: Review existing strategies and their performance

1. get_all_strategies(include_latest_backtest=true)
   → See all strategies with performance data

2. Identify interesting strategies:
   → High Sharpe ratio (>1.5)
   → Acceptable drawdown (<20%)
   → Realistic win rate (45-65%)

3. get_strategy_code(strategy_name="TopPerformer")
   → Review implementation details
   → Understand why it performs well

4. get_strategy_versions(base_strategy_name="TopPerformer")
   → See how strategy evolved
   → Identify what improvements were made

5. Apply learnings:
   → Use as template for new strategies
   → Or enhance further with improve-trading-strategies skill

Cost: Free to $0.003

目标: 查看现有策略及其表现

1. get_all_strategies(include_latest_backtest=true)
   → 查看所有带表现数据的策略

2. 识别有潜力的策略:
   → 高夏普比率（>1.5）
   → 可接受的回撤（<20%）
   → 合理的胜率（45-65%）

3. get_strategy_code(strategy_name="TopPerformer")
   → 查看实现细节
   → 了解其表现优秀的原因

4. get_strategy_versions(base_strategy_name="TopPerformer")
   → 查看策略的演变过程
   → 识别做出了哪些改进

5. 应用所学:
   → 作为新策略的模板
   → 或使用improve-trading-strategies技能进一步优化

成本: 免费至0.003美元

Workflow 3: Data Coverage Check

工作流3：数据覆盖检查

Goal: Verify data availability before backtesting

1. Choose your strategy parameters:
   Symbol: BTC-USDT
   Timeframe: 1h
   Test period: 6 months

2. get_data_availability(symbols=["BTC-USDT"], only_with_data=true)
   Returns: "BTC-USDT available from 2020-01-01 to 2025-02-02"

3. Verify coverage:
   ✓ Has 6+ months of data
   ✓ Covers desired test period
   ✓ Ready to backtest

4. If insufficient data:
   → Try shorter test period
   → Or choose different symbol (BTC-USDT and ETH-USDT have longest history)

5. Proceed to testing:
   → Use test-trading-strategies skill to run backtest

Cost: $0.001

目标: 回测前验证数据可用性

1. 选择你的策略参数:
   交易对: BTC-USDT
   时间框架: 1小时
   测试周期: 6个月

2. get_data_availability(symbols=["BTC-USDT"], only_with_data=true)
   返回值: "BTC-USDT可用数据范围为2020-01-01至2025-02-02"

3. 验证覆盖范围:
   ✓ 拥有6个月以上的数据
   ✓ 覆盖所需测试周期
   ✓ 可进行回测

4. 若数据不足:
   → 尝试缩短测试周期
   → 或选择其他交易对（BTC-USDT和ETH-USDT的历史数据最久）

5. 开始测试:
   → 使用test-trading-strategies技能运行回测

成本: 0.001美元

Workflow 4: Indicator Research

工作流4：指标研究

Goal: Find the right indicators for your strategy concept

Strategy Concept: Mean reversion on cryptocurrency

1. get_all_technical_indicators(category="momentum")
   → Browse momentum indicators (RSI, Stochastic, etc.)

2. get_all_technical_indicators(category="volatility")
   → Browse volatility indicators (Bollinger Bands, ATR, etc.)

3. Select indicators for mean reversion:
   → RSI (identify overbought/oversold)
   → Bollinger Bands (measure deviation from mean)
   → ATR (position sizing based on volatility)

4. Note exact indicator names:
   → "RSI" (not "rsi" or "RelativeStrengthIndex")
   → "BollingerBands" (not "BB" or "bollinger")
   → "ATR" (not "AverageTrueRange")

5. Use exact names in strategy description:
   → When using build-trading-strategies skill
   → Reference indicators precisely: "Use RSI with period 14"

Cost: $0.002

目标: 为你的策略概念选择合适的指标

策略概念: 加密货币均值回归

1. get_all_technical_indicators(category="momentum")
   → 浏览动量类指标（RSI、Stochastic等）

2. get_all_technical_indicators(category="volatility")
   → 浏览波动率类指标（布林带、ATR等）

3. 选择均值回归所需的指标:
   → RSI（识别超买/超卖）
   → 布林带（衡量偏离均值的程度）
   → ATR（基于波动率计算仓位大小）

4. 记录指标的精确名称:
   → "RSI"（不要用"rsi"或"RelativeStrengthIndex"）
   → "BollingerBands"（不要用"BB"或"bollinger"）
   → "ATR"（不要用"AverageTrueRange"）

5. 策略描述中使用精确名称:
   → 使用build-trading-strategies技能时
   → 精确引用指标："使用周期为14的RSI"

成本: 0.002美元

Advanced Usage

高级用法

Filtering and Optimization

过滤与优化

Efficient querying:

undefined

高效查询:

undefined

Get only active symbols

仅获取活跃交易对

get_all_symbols(active_only=true)

Filter indicators by category

按类别过滤指标

get_all_technical_indicators(category="momentum")

Check specific symbols only

仅检查特定交易对

get_data_availability(symbols=["BTC-USDT", "ETH-USDT"], only_with_data=true)

Limit backtest results

限制回测结果数量

get_latest_backtest_results(limit=5)

undefined

get_latest_backtest_results(limit=5)

undefined

Backtest Result Analysis

回测结果分析

Detailed equity curve analysis:

get_latest_backtest_results(
    strategy_name="MyStrategy",
    include_equity_curve=true,
    equity_curve_max_points=500
)

Returns equity curve data for visualizing strategy performance over time.

Use cases:

Identify periods of strong/weak performance
Detect regime changes (strategy works in trending vs ranging markets)
Compare multiple strategies visually

详细权益曲线分析:

get_latest_backtest_results(
    strategy_name="MyStrategy",
    include_equity_curve=true,
    equity_curve_max_points=500
)

返回权益曲线数据，用于可视化策略随时间的表现。

适用场景:

识别表现强劲/疲软的时期
检测市场状态变化（策略在趋势市和震荡市的表现差异）
可视化对比多个策略

Cross-Asset Research

跨资产研究

Compare data availability across assets:

1. get_data_availability(data_type="crypto", only_with_data=true)
   → See all crypto pairs with data

2. Compare:
   - Which symbols have longest history?
   - Which symbols have most recent backfills?
   - Which timeframes are well-covered?

3. Choose optimal symbols for strategy development:
   → BTC-USDT, ETH-USDT: Longest history, most reliable
   → Altcoins: Shorter history, higher risk, potentially higher returns

对比不同资产的数据可用性:

1. get_data_availability(data_type="crypto", only_with_data=true)
   → 查看所有有数据的加密货币交易对

2. 对比:
   - 哪些交易对的历史数据最久？
   - 哪些交易对的补全数据最新？
   - 哪些时间框架的覆盖最完善？

3. 为策略开发选择最优交易对:
   → BTC-USDT、ETH-USDT：历史数据最久，最可靠
   → 山寨币：历史数据较短，风险较高，潜在收益也更高

Troubleshooting

故障排除

"No Strategies Found"

"未找到策略"

Issue:

get_all_strategies

returns empty list

Solutions:

Strategies are linked to your API key's wallet
Ensure you're using the correct API key
If new account, you haven't created strategies yet (use build-trading-strategies skill to create first strategy)

问题:

get_all_strategies

返回空列表

解决方案:

策略与你的API密钥关联的钱包绑定
确保使用正确的API密钥
若为新账户，你还未创建任何策略（使用build-trading-strategies技能创建首个策略）

"Symbol Not Found"

"未找到交易对"

Issue:

get_data_availability

doesn't show expected symbol

Solutions:

Use
```
get_all_symbols()
```
to see complete list of available symbols
Check spelling (BTC-USDT not BTC-USD or BTCUSDT)
Some symbols may not have data backfilled yet (check
```
active_only=false
```
to see inactive symbols)

问题:

get_data_availability

未显示预期的交易对

解决方案:

使用
```
get_all_symbols()
```
查看所有可用交易对
检查拼写（BTC-USDT而非BTC-USD或BTCUSDT）
部分交易对可能尚未补全数据（设置
```
active_only=false
```
查看非活跃交易对）

"No Indicator Matches Description"

"未找到匹配的指标"

Issue: Can't find indicator you're looking for

Solutions:

Use

get_all_technical_indicators(category="all")

to browse complete list

Search for similar names (RSI vs RelativeStrengthIndex)
Check category filter (momentum indicator won't show if filtering by trend)
Jesse framework uses specific names - use exact names returned by tool

问题: 找不到你要的指标

解决方案:

使用

get_all_technical_indicators(category="all")

浏览完整列表

搜索类似名称（RSI vs RelativeStrengthIndex）
检查类别过滤器（动量类指标在过滤趋势类时不会显示）
Jesse框架使用特定名称，请使用工具返回的精确名称

"Backtest Results Missing"

"回测结果缺失"

Issue:

get_latest_backtest_results

doesn't show expected backtest

Solutions:

Check strategy name spelling (case-sensitive)
Backtest may still be running (wait 20-60 seconds)
Backtest may have failed (check for error messages)
Use
```
limit
```
parameter to retrieve more results (default is 10)

问题:

get_latest_backtest_results

未显示预期的回测结果

解决方案:

检查策略名称的拼写（区分大小写）
回测可能仍在运行（等待20-60秒）
回测可能失败（查看错误信息）
使用
```
limit
```
参数获取更多结果（默认10条）

Next Steps

后续步骤

After exploring data with this skill:

Generate strategy ideas:

Use
```
design-trading-strategies
```
skill to generate AI-powered strategy concepts
Cost: $0.05-$1.00 per idea generation (cheapest AI tool)
Best when: You want to explore creative concepts before committing to development

Build strategies directly:

Use
```
build-trading-strategies
```
skill to generate complete strategy code
Cost: $1.00-$4.50 per strategy (most expensive AI tool)
Best when: You already know what you want to build

Test existing strategies:

Use
```
test-trading-strategies
```
skill to backtest strategies
Cost: $0.001 per backtest
Best when: You have strategy code and want to validate performance

Improve strategies:

Use
```
improve-trading-strategies
```
skill to refine, optimize, or enhance with ML
Cost: $0.50-$4.00 per operation
Best when: You have an existing strategy that needs improvement

Prediction market trading:

Use
```
trade-prediction-markets
```
skill for Polymarket YES/NO token strategies
Cost: $0.001-$4.50 depending on operation
Best when: You want to trade on real-world events (politics, economics, sports)

使用此技能探索数据后:

生成策略思路:

使用
```
design-trading-strategies
```
技能生成AI驱动的策略概念
成本: 每个思路0.05-1.00美元（最便宜的AI工具）
最佳场景: 你想在投入开发前探索创意概念

直接构建策略:

使用
```
build-trading-strategies
```
技能生成完整的策略代码
成本: 每个策略1.00-4.50美元（最昂贵的AI工具）
最佳场景: 你已经明确要构建的内容

测试现有策略:

使用
```
test-trading-strategies
```
技能回测策略
成本: 每次回测0.001美元
最佳场景: 你已有策略代码，想要验证其表现

优化策略:

使用
```
improve-trading-strategies
```
技能优化、改进或集成ML功能
成本: 每次操作0.50-4.00美元
最佳场景: 你已有策略，需要进行优化

预测市场交易:

使用
```
trade-prediction-markets
```
技能进行Polymarket YES/NO代币交易策略
成本: 0.001-4.50美元，取决于操作类型
最佳场景: 你想针对现实世界事件（政治、经济、体育）进行交易

Summary

总结

This skill provides fast, cheap, read-only access to Robonet's trading resources:

8 data tools covering strategies, symbols, indicators, ML topics, and backtest results
<1 second execution for all tools
Free to $0.001 cost (essentially free to explore)
Zero risk (read-only operations, no modifications or executions)

Core principle: Explore thoroughly before building. Spending 2-3 minutes browsing data (costs <$0.01) can save dollars in wasted strategy generation and prevent costly mistakes.

Best practice: Start every workflow with this skill, then progress to design → build → improve → test → deploy based on your findings.

此技能提供快速、低成本的只读访问权限，用于探索Robonet的交易资源:

8个数据工具，覆盖策略、交易对、指标、ML主题及回测结果
所有工具执行时间<1秒
成本免费至0.001美元（探索几乎无成本）
零风险（只读操作，无修改或执行）

核心原则: 构建前充分探索。花2-3分钟浏览数据（成本<0.01美元）可节省因策略生成失误产生的数美元成本，并避免代价高昂的错误。

最佳实践: 每个工作流都从使用此技能开始，然后根据探索结果逐步进行设计→构建→优化→测试→部署。