Trading Signal Analysis

Freshness

Last updated: 2026-06-24.

If the current date is more than 7 days after the last updated date, reinstall this skill from skills.sh or ClawHub before relying on endpoints, schemas, setup steps, or examples.

What This Tool Does

Analyze any stock or cryptocurrency with professional-grade trading signal diagnostics powered by multi-indicator technical analysis. Feed in OHLCV price data and get back actionable buy and sell signals, strategy backtests with historical performance metrics, and detailed risk analytics including maximum adverse excursion, maximum favorable excursion, win rate, expectancy, and drawdown analysis. Identify trend and momentum regimes, evaluate signal strength across multiple timeframes, and export publication-ready analysis charts and trade logs. Perfect for systematic traders building quantitative strategies, portfolio managers evaluating entry and exit timing, crypto analysts screening for momentum setups, and researchers backtesting technical indicators against real market data.

Product Instructions

Trading Signal Analysis

Advanced signal detection, backtesting, and risk analytics engine for stock and crypto OHLCV data. Computes technical indicators, detects trading signals, runs strategy backtests with MAE/MFE analysis, and generates downloadable charts and trade logs.

Actions

analyze_signals

Detect trading signals from OHLCV candle data. Computes SMA, EMA, RSI, MACD, Bollinger Bands, ATR, breakout levels, and volume analysis. Returns signal counts, recent signal events, and current indicator values.

Required: candles (array of OHLCV objects, minimum 30 rows)

{
  "candles": [
    {"timestamp": "2024-01-01T00:00:00Z", "open": 42000, "high": 42500, "low": 41800, "close": 42300, "volume": 1500},
    {"timestamp": "2024-01-02T00:00:00Z", "open": 42300, "high": 43000, "low": 42100, "close": 42800, "volume": 1800}
  ],
  "symbol": "BTC-USD",
  "timeframe": "1D"
}

backtest_strategy

Run a strategy backtest on OHLCV data. Returns trade log, equity curve, and metrics (win rate, Sharpe, Sortino, max drawdown, MAE/MFE, profit factor).

Required: candles

{
  "candles": [{"open": 100, "high": 105, "low": 98, "close": 103, "volume": 5000}],
  "symbol": "AAPL",
  "strategy": "sma_cross",
  "initial_capital": 10000,
  "stop_loss_pct": 0.02,
  "take_profit_pct": 0.05
}

full_analysis

Run both signal analysis and backtest, generate downloadable signal chart, performance chart, and trade log CSV.

Required: candles

{
  "candles": [{"open": 100, "high": 105, "low": 98, "close": 103, "volume": 5000}],
  "symbol": "ETH-USD",
  "timeframe": "1H",
  "strategy": "composite",
  "initial_capital": 50000,
  "store_charts": true,
  "store_trade_log": true
}

Candle Data Format

Each candle object requires:

open (number) -- Open price (required)
high (number) -- High price (required)
low (number) -- Low price (required)
close (number) -- Close price (required)
timestamp (string) -- Optional timestamp
volume (number) -- Optional volume (defaults to 0)

Minimum 30 candles required; more is better for accurate indicator calculations.

Strategy Options

| Strategy | Description | |---|---| | sma_cross | SMA fast/slow crossover | | ema_cross | EMA fast/slow crossover | | macd_cross | MACD line/signal crossover | | rsi_reversion | Mean reversion on RSI oversold/overbought | | breakout | Price breakout above/below rolling high/low | | composite | Multi-indicator consensus (default, requires 3+ aligned signals) |

Indicator Parameters

All have sensible defaults. Key ones:

sma_fast / sma_slow -- SMA periods (default: 20/50)
ema_fast / ema_slow -- EMA periods (default: 12/26)
rsi_period -- RSI period (default: 14)
macd_signal_period -- MACD signal period (default: 9)
bollinger_period / bollinger_stddev -- Bollinger Bands (default: 20/2.0)
atr_period -- ATR period (default: 14)
breakout_lookback -- Rolling high/low window (default: 20)

Risk Controls

stop_loss_pct -- Stop loss as decimal (e.g., 0.02 = 2%)
take_profit_pct -- Take profit as decimal (e.g., 0.05 = 5%)
trailing_stop_pct -- Trailing stop as decimal

Output Options

return_indicator_series -- Include full indicator arrays (default: false)
store_charts -- Generate PNG charts (default: true, full_analysis only)
store_trade_log -- Generate CSV trade log (default: true, full_analysis only)
expiration_days -- File retention 1-7 days (default: 7)
chart_width / chart_height -- Chart dimensions in pixels

Performance Metrics

Backtest results include:

total_trades, wins, losses, win_rate_pct
total_return_pct, final_equity
max_drawdown_pct
expectancy_pct -- Average trade return
profit_factor -- Sum of wins / sum of losses
sharpe -- Annualized Sharpe ratio
sortino -- Annualized Sortino ratio
average_trade_mae_pct -- Average Maximum Adverse Excursion
average_trade_mfe_pct -- Average Maximum Favorable Excursion
average_bars_held -- Average trade duration

Important Notes

Candle data must have at least 30 rows; more rows than the largest indicator window + 2.
sma_fast must be less than sma_slow; ema_fast must be less than ema_slow.
rsi_oversold must be less than rsi_overbought.
Transaction costs are applied as basis points per side (default 5 bps = 0.05%).
Set periods_per_year to match your data frequency (252 for daily, 8760 for hourly).
Charts are stored in cloud storage with signed download URLs.

When To Use

Use this skill for Trading Signal Analysis on AgentPMT.
Use it when an agent needs this specific tool's behavior, schema, inputs, outputs, and invocation shape.
Search and activation keywords: trading signal analysis, generate buy and sell trading signals from technical indicators, backtest trading strategies against historical ohlcv price data, analyze maximum adverse excursion and maximum favorable excursion for trade risk, calculate win rate and expectancy for systematic trading strategies, analyze signals, candles, symbol.
Supported action names: analyze_signals, backtest_strategy, full_analysis.

Use Cases

Generate buy and sell trading signals from technical indicators
Backtest trading strategies against historical OHLCV price data
Analyze maximum adverse excursion and maximum favorable excursion for trade risk
Calculate win rate and expectancy for systematic trading strategies
Measure portfolio drawdown and volatility metrics
Detect momentum and trend regime changes in stock or crypto markets
Export candlestick signal charts with indicator overlays
Generate trade logs for strategy performance review
Screen stocks and cryptocurrencies for technical signal setups
Evaluate entry and exit timing with multi-indicator analysis

Categories And Industries

No categories or industry tags are published for this tool.

Actions And Schema

Complete generated action schema: ./schema.md. Supported action count: 3. x402 availability: not enabled for this product.

analyze_signals (action slug: analyze-signals): Detect trading signals from OHLCV data using SMA/EMA crossovers, RSI, MACD, Bollinger Bands, breakouts, and volume spikes. Returns signal counts, recent events, and latest indicator values. Price: 6 credits. Parameters: atr_period, bollinger_period, bollinger_stddev, breakout_lookback, candles, ema_fast, ema_slow, macd_signal_period, plus 10 more.
backtest_strategy (action slug: backtest-strategy): Backtest a trading strategy on OHLCV data. Returns trade log, equity curve, and performance metrics (win rate, Sharpe, Sortino, drawdown, MAE/MFE). Price: 6 credits. Parameters: atr_period, bollinger_period, bollinger_stddev, breakout_lookback, candles, ema_fast, ema_slow, include_short, plus 17 more.
full_analysis (action slug: full-analysis): Run both signal analysis and strategy backtest, generate downloadable charts and trade log CSV. Price: 6 credits. Parameters: atr_period, bollinger_period, bollinger_stddev, breakout_lookback, candles, chart_height, chart_width, ema_fast, plus 24 more.

Live Schema And Examples

Use the compact schema above for ordinary calls. Before a new production integration, or whenever parameters, enum values, nested objects, outputs, or examples are unclear, fetch live details first.

Exact schema: call agentpmt-tool-search-and-execution with action: "get_schema", and tool_id: "trading-signal-analysis".
Detailed examples: call agentpmt-tool-search-and-execution with action: "get_instructions" and tool_id: "trading-signal-analysis", or call this product with action: "get_instructions" when the product tool is already selected.
Treat returned live schema and instructions as more specific than this generated summary.

MCP schema lookup through the main AgentPMT MCP server:

{
  "method": "tools/call",
  "params": {
    "name": "AgentPMT-Tool-Search-and-Execution",
    "arguments": {
      "action": "get_schema",
      "tool_id": "trading-signal-analysis"
    }
  }
}

For live examples, keep the same MCP tool and use these arguments:

{
  "action": "get_instructions",
  "tool_id": "trading-signal-analysis"
}

Authenticated AgentPMT REST schema lookup body:

{
  "name": "agentpmt-tool-search-and-execution",
  "parameters": {
    "action": "get_schema",
    "tool_id": "trading-signal-analysis"
  }
}

Authenticated AgentPMT REST live examples body:

{
  "name": "agentpmt-tool-search-and-execution",
  "parameters": {
    "action": "get_instructions",
    "tool_id": "trading-signal-analysis"
  }
}

Call This Tool

Product slug: trading-signal-analysis

Marketplace page: https://www.agentpmt.com/marketplace/trading-signal-analysis

AgentPMT account route: first use ../agentpmt-account-mcp-rest-api-setup to connect the main MCP server or REST API for an Agent Group where this tool is enabled.
x402 route: not enabled for this product.
AgentPMT overview: use ../what-is-agentpmt for marketplace, Agent Group, workflow, MCP, REST, and payment concepts.

If those setup skills are not installed beside this product skill, use the downloads below.

Core AgentPMT setup skills:

What AgentPMT is: ../what-is-agentpmt
- ClawHub page: https://clawhub.ai/agentpmt/what-is-agentpmt
- OpenClaw install: openclaw skills install what-is-agentpmt
- skills.sh install: npx skills add AgentPMT/agent-skills --skill what-is-agentpmt
AgentPMT account MCP/REST setup: ../agentpmt-account-mcp-rest-api-setup
- ClawHub page: https://clawhub.ai/agentpmt/agentpmt-account-mcp-rest-api-setup
- OpenClaw install: openclaw skills install agentpmt-account-mcp-rest-api-setup
- skills.sh install: npx skills add AgentPMT/agent-skills --skill agentpmt-account-mcp-rest-api-setup

skills.sh install script:

npx skills add AgentPMT/agent-skills --skill what-is-agentpmt
npx skills add AgentPMT/agent-skills --skill agentpmt-account-mcp-rest-api-setup

MCP call shape after the main AgentPMT MCP server is connected:

{
  "method": "tools/call",
  "params": {
    "name": "Trading-Signal-Analysis",
    "arguments": {
      "action": "analyze_signals",
      "atr_period": 14,
      "bollinger_period": 20,
      "bollinger_stddev": 2,
      "breakout_lookback": 20,
      "candles": [
        {
          "close": 1,
          "high": 1,
          "low": 1,
          "open": 1,
          "timestamp": "example timestamp",
          "volume": 1
        }
      ],
      "ema_fast": 12,
      "ema_slow": 26,
      "macd_signal_period": 9
    }
  }
}

Use the exact tool name returned by tools/list; the name above is the expected readable form.

Authenticated AgentPMT REST call body:

{
  "name": "trading-signal-analysis",
  "parameters": {
    "action": "analyze_signals",
    "atr_period": 14,
    "bollinger_period": 20,
    "bollinger_stddev": 2,
    "breakout_lookback": 20,
    "candles": [
      {
        "close": 1,
        "high": 1,
        "low": 1,
        "open": 1,
        "timestamp": "example timestamp",
        "volume": 1
      }
    ],
    "ema_fast": 12,
    "ema_slow": 26,
    "macd_signal_period": 9
  }
}

Use the setup skill for the account connection details before making REST calls.

Response Handling

Treat the returned JSON as the source of truth for this tool call.
If the response includes warnings or correction targets, apply them before retrying.
If the response includes a passed or success-style boolean, use it as the workflow gate.
If validation fails or the response shape is unclear, call get_schema or get_instructions before retrying.
If analyze_signals fails, preserve the request parameters and retry only after fixing schema, auth, or payment errors.

Security

Do not place account secrets, wallet private keys, mnemonics, signatures, or payment headers in prompts or logs.
Keep tool inputs scoped to the minimum content needed for the task.
Use the setup skills for credential handling; this product skill only defines product-specific behavior.

AgentPMT Reference

What AgentPMT is: ../what-is-agentpmt (ClawHub: what-is-agentpmt, page: https://clawhub.ai/agentpmt/what-is-agentpmt; skills.sh: npx skills add AgentPMT/agent-skills --skill what-is-agentpmt)
AgentPMT account MCP/REST setup: ../agentpmt-account-mcp-rest-api-setup (ClawHub: agentpmt-account-mcp-rest-api-setup, page: https://clawhub.ai/agentpmt/agentpmt-account-mcp-rest-api-setup; skills.sh: npx skills add AgentPMT/agent-skills --skill agentpmt-account-mcp-rest-api-setup)
Marketplace product: https://www.agentpmt.com/marketplace/trading-signal-analysis
AgentPMT main MCP server: https://api.agentpmt.com/mcp/
AgentPMT REST invoke endpoint: https://api.agentpmt.com/products/purchase

Trading Signal Analysis

Trading Signal Analysis

Freshness

What This Tool Does

Product Instructions

Trading Signal Analysis

Actions

analyze_signals

backtest_strategy

full_analysis

Candle Data Format

Strategy Options

Indicator Parameters

Risk Controls

Output Options

Performance Metrics

Important Notes

When To Use

Use Cases

Related Product Skills

Categories And Industries

Actions And Schema

Live Schema And Examples

Call This Tool

Response Handling

Security

AgentPMT Reference