Name: Trading Signal Analysis
Brand: Apoth3osis
SKU: 69858a63269243768b447d69
Price: 0.06 USD
Availability: InStock

Trading Signal Analysis

Available Actions

analyze_signalsbacktest_strategyfull_analysis

Details

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.

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

Actions(3)

analyze_signals18 params(1 required)

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.

candlesrequiredarray

OHLCV candle series (minimum 30 rows). Each item needs open, high, low, close (required) and optional timestamp and volume.

Array of: object

symbolstring

Optional symbol label (e.g., BTC-USD).

timeframestring

Optional timeframe label (e.g., 1H, 1D).

sma_fastinteger

SMA fast period.

Default: 20

sma_slowinteger

SMA slow period.

Default: 50

ema_fastinteger

EMA fast period.

Default: 12

ema_slowinteger

EMA slow period.

Default: 26

rsi_periodinteger

RSI period.

Default: 14

macd_signal_periodinteger

MACD signal line period.

Default: 9

bollinger_periodinteger

Bollinger Bands period.

Default: 20

bollinger_stddevnumber

Bollinger Bands standard deviation multiplier.

Default: 2

atr_periodinteger

ATR period.

Default: 14

breakout_lookbackinteger

Breakout lookback period.

Default: 20

volume_windowinteger

Volume SMA window.

Default: 20

rsi_oversoldnumber

RSI oversold threshold.

Default: 30

rsi_overboughtnumber

RSI overbought threshold.

Default: 70

volume_spike_multipliernumber

Volume spike detection multiplier.

Default: 1.5

return_indicator_seriesboolean

Return full indicator arrays in response.

backtest_strategy25 params(1 required)

Backtest a trading strategy on OHLCV data. Returns trade log, equity curve, and performance metrics (win rate, Sharpe, Sortino, drawdown, MAE/MFE).

candlesrequiredarray

OHLCV candle series (minimum 30 rows).

Array of: object

symbolstring

Optional symbol label.

timeframestring

Optional timeframe label.

strategystring

Backtest strategy to use.

Values:

sma_crossema_crossmacd_crossrsi_reversionbreakoutcomposite

Default: composite

include_shortboolean

Allow short entries.

initial_capitalnumber

Starting equity.

Default: 10000

transaction_cost_bpsnumber

Round-trip transaction cost in basis points per side.

Default: 5

max_tradesinteger

Maximum number of closed trades.

Default: 1000

sma_fastinteger

SMA fast period.

Default: 20

sma_slowinteger

SMA slow period.

Default: 50

ema_fastinteger

EMA fast period.

Default: 12

ema_slowinteger

EMA slow period.

Default: 26

rsi_periodinteger

RSI period.

Default: 14

macd_signal_periodinteger

MACD signal line period.

Default: 9

bollinger_periodinteger

Bollinger Bands period.

Default: 20

bollinger_stddevnumber

Bollinger Bands standard deviation.

Default: 2

atr_periodinteger

ATR period.

Default: 14

breakout_lookbackinteger

Breakout lookback period.

Default: 20

volume_windowinteger

Volume SMA window.

Default: 20

rsi_oversoldnumber

RSI oversold threshold.

Default: 30

rsi_overboughtnumber

RSI overbought threshold.

Default: 70

stop_loss_pctnumber

Stop loss percentage (0.001 to 0.95).

take_profit_pctnumber

Take profit percentage (0.001 to 5.0).

trailing_stop_pctnumber

Trailing stop percentage (0.001 to 0.95).

periods_per_yearinteger

Annualization factor for Sharpe/Sortino (252 for daily, 8760 for hourly).

Default: 252

full_analysis32 params(1 required)

Run both signal analysis and strategy backtest, generate downloadable charts and trade log CSV.

candlesrequiredarray

OHLCV candle series (minimum 30 rows).

Array of: object

symbolstring

Optional symbol label.

timeframestring

Optional timeframe label.

strategystring

Backtest strategy to use.

Values:

sma_crossema_crossmacd_crossrsi_reversionbreakoutcomposite

Default: composite

include_shortboolean

Allow short entries.

initial_capitalnumber

Starting equity.

Default: 10000

transaction_cost_bpsnumber

Transaction cost in basis points.

Default: 5

max_tradesinteger

Maximum closed trades.

Default: 1000

sma_fastinteger

SMA fast period.

Default: 20

sma_slowinteger

SMA slow period.

Default: 50

ema_fastinteger

EMA fast period.

Default: 12

ema_slowinteger

EMA slow period.

Default: 26

rsi_periodinteger

RSI period.

Default: 14

macd_signal_periodinteger

MACD signal line period.

Default: 9

bollinger_periodinteger

Bollinger Bands period.

Default: 20

bollinger_stddevnumber

Bollinger Bands standard deviation.

Default: 2

atr_periodinteger

ATR period.

Default: 14

breakout_lookbackinteger

Breakout lookback.

Default: 20

volume_windowinteger

Volume SMA window.

Default: 20

rsi_oversoldnumber

RSI oversold threshold.

Default: 30

rsi_overboughtnumber

RSI overbought threshold.

Default: 70

volume_spike_multipliernumber

Volume spike multiplier.

Default: 1.5

stop_loss_pctnumber

Stop loss percentage.

take_profit_pctnumber

Take profit percentage.

trailing_stop_pctnumber

Trailing stop percentage.

return_indicator_seriesboolean

Return full indicator arrays.

store_chartsboolean

Generate and store signal and performance charts.

Default: true

store_trade_logboolean

Store trade log as downloadable CSV.

Default: true

expiration_daysinteger

Cloud file retention in days (1-7).

Default: 7

chart_widthinteger

Chart width in pixels.

Default: 1400

chart_heightinteger

Chart height in pixels.

Default: 900

periods_per_yearinteger

Annualization factor for Sharpe/Sortino.

Default: 252

curl -X POST "https://api.agentpmt.com/products/purchase" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer ********" \
  -d '{
    "product_id": "69858a63269243768b447d69",
    "parameters": {
      "action": "analyze_signals",
      "candles": [],
      "sma_fast": 20,
      "sma_slow": 50,
      "ema_fast": 12,
      "ema_slow": 26,
      "rsi_period": 14,
      "macd_signal_period": 9,
      "bollinger_period": 20,
      "bollinger_stddev": 2,
      "atr_period": 14,
      "breakout_lookback": 20,
      "volume_window": 20,
      "rsi_oversold": 30,
      "rsi_overbought": 70,
      "volume_spike_multiplier": 1.5
    }
  }'

import requests
import json

url = "https://api.agentpmt.com/products/purchase"

headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer ********"
}

data = {
    "product_id": "69858a63269243768b447d69",
    "parameters": {
        "action": "analyze_signals",
        "candles": [],
        "sma_fast": 20,
        "sma_slow": 50,
        "ema_fast": 12,
        "ema_slow": 26,
        "rsi_period": 14,
        "macd_signal_period": 9,
        "bollinger_period": 20,
        "bollinger_stddev": 2,
        "atr_period": 14,
        "breakout_lookback": 20,
        "volume_window": 20,
        "rsi_oversold": 30,
        "rsi_overbought": 70,
        "volume_spike_multiplier": 1.5
    }
}

response = requests.post(url, headers=headers, json=data)
print(response.status_code)
print(response.json())

const url = "https://api.agentpmt.com/products/purchase";

const headers = {
  "Content-Type": "application/json",
  "Authorization": "Bearer ********"
};

const data = {
  product_id: "69858a63269243768b447d69",
  parameters: {
    "action": "analyze_signals",
    "candles": [],
    "sma_fast": 20,
    "sma_slow": 50,
    "ema_fast": 12,
    "ema_slow": 26,
    "rsi_period": 14,
    "macd_signal_period": 9,
    "bollinger_period": 20,
    "bollinger_stddev": 2,
    "atr_period": 14,
    "breakout_lookback": 20,
    "volume_window": 20,
    "rsi_oversold": 30,
    "rsi_overbought": 70,
    "volume_spike_multiplier": 1.5
  }
};

fetch(url, {
  method: "POST",
  headers: headers,
  body: JSON.stringify(data)
})
  .then(response => response.json())
  .then(data => console.log(data))
  .catch(error => console.error("Error:", error));

const axios = require('axios');

const url = "https://api.agentpmt.com/products/purchase";

const headers = {
  "Content-Type": "application/json",
  "Authorization": "Bearer ********"
};

const data = {
  product_id: "69858a63269243768b447d69",
  parameters: {
    "action": "analyze_signals",
    "candles": [],
    "sma_fast": 20,
    "sma_slow": 50,
    "ema_fast": 12,
    "ema_slow": 26,
    "rsi_period": 14,
    "macd_signal_period": 9,
    "bollinger_period": 20,
    "bollinger_stddev": 2,
    "atr_period": 14,
    "breakout_lookback": 20,
    "volume_window": 20,
    "rsi_oversold": 30,
    "rsi_overbought": 70,
    "volume_spike_multiplier": 1.5
  }
};

axios.post(url, data, { headers })
  .then(response => {
    console.log(response.status);
    console.log(response.data);
  })
  .catch(error => {
    console.error("Error:", error.message);
  });

Login to view your API and budget keys. The example above uses placeholder values. Sign in to see personalized code with your bearer token.

This tool supports credit-based access for external agents using AgentAddress identities or standard crypto wallets. External agents should use the External Agent API to buy credits with x402 and invoke this tool.

1. Buy Credits

Purchase credits via x402 payment (500 credit minimum, 100 credits = $1).

# Request payment requirements (returns 402 + PAYMENT-REQUIRED header)
curl -i -s -X POST "https://www.agentpmt.com/api/external/credits/purchase" \
  -H "Content-Type: application/json" \
  -d '{ "wallet_address":"0xYOUR_WALLET", "credits": 500, "payment_method":"x402" }'

# Sign the EIP-3009 authorization, then retry with signature header
curl -s -X POST "https://www.agentpmt.com/api/external/credits/purchase" \
  -H "Content-Type: application/json" \
  -H "PAYMENT-SIGNATURE: <base64-json>" \
  -d '{ "wallet_address":"0xYOUR_WALLET", "credits": 500, "payment_method":"x402" }'

2. Create a Session Nonce (nonce used in signed balance/invoke)

curl -s -X POST "https://www.agentpmt.com/api/external/auth/session" \
  -H "Content-Type: application/json" \
  -d '{ "wallet_address":"0xYOUR_WALLET" }'

3. Invoke This Tool

Sign the message with your wallet (EIP-191 personal-sign), then POST to the invoke endpoint.

# Sign this message (wallet MUST be lowercased):
# agentpmt-external
# wallet:0xyourwallet...
# session:<session_nonce>
# request:<request_id>
# action:invoke
# product:69858a63269243768b447d69
# payload:<sha256(canonical_json(parameters))>

curl -s -X POST "https://www.agentpmt.com/api/external/tools/69858a63269243768b447d69/invoke" \
  -H "Content-Type: application/json" \
  -d '{
    "wallet_address": "0xYOUR_WALLET",
    "session_nonce": "<session_nonce>",
    "request_id": "invoke-uuid",
    "signature": "0x<signature>",
    "parameters": {
      "action": "your_action",
      "your_param": "value"
    }
  }'

About The Developer

Apoth3osis

★15 stars

Joined Agent Payment: August 14, 2025

We build tools that enable AI agents to excel in the mathematical realm.

Our small team develops experimental and unique solutions in the AI arena, with a strong focus on modular computing for agentic applications and custom model deployment. We have handled projects for a variety of applications across many sectors, from algorithmic trading and financial analysis, to molecular simulations and predictions, to habitat and biodiversity monitoring and wildlife conservation.

Trading Signal Analysis

Description