tmai-api 0.3.0

Python SDK for Token Metrics AI API

Token Metrics AI API Python SDK

The official Python SDK for Token Metrics AI API - providing professional investors and traders with comprehensive cryptocurrency analysis, AI-powered trading signals, market data, and advanced insights.

Features

• Comprehensive Market Data: Access detailed information on thousands of cryptocurrencies
• AI-Powered Analysis: Get trading and investment grades based on advanced AI models
• Technical Indicators: Access technical analysis grades and quantitative metrics
• Price Data: Retrieve historical OHLCV (Open, High, Low, Close, Volume) data
• Trading Signals: Receive AI-generated long and short trading signals
• AI Agent: Interact with Token Metrics' AI chatbot for market insights
• AI Reports: Access detailed technical, fundamental, and trading reports
• Simple Interface: Intuitive API with Pandas DataFrame integration

Installation

pip install tmai-api

Quick Start

from tmai_api import TokenMetricsClient

# Initialize the client with your API key
client = TokenMetricsClient(api_key="your-api-key")

# Get information for top cryptocurrencies
tokens = client.tokens.get(symbol="BTC,ETH")

# Get short-term trading grades
trader_grades = client.trader_grades.get(
    symbol="BTC,ETH",
    startDate="2023-10-01",
    endDate="2023-10-10"
)

# Get long-term investment grades
investor_grades = client.investor_grades.get(
    symbol="BTC,ETH", 
    startDate="2023-10-01",
    endDate="2023-10-10"
)

# Ask the AI agent a question
answer = client.ai_agent.get_answer_text("What is your analysis of Bitcoin?")

Data Analysis with Pandas

Convert API responses directly to Pandas DataFrames for easy analysis:

# Convert to DataFrame
tokens_df = client.tokens.get_dataframe(symbol="BTC,ETH")
trader_grades_df = client.trader_grades.get_dataframe(symbol="BTC,ETH")

# Analyze with Pandas
import pandas as pd

# Filter for specific tokens
bitcoin_data = trader_grades_df[trader_grades_df['TOKEN_SYMBOL'] == 'BTC']

# Plot grades over time
import matplotlib.pyplot as plt
bitcoin_data.plot(x='DATE', y='TM_TRADER_GRADE', figsize=(10, 6))
plt.title('Bitcoin Trading Grade Trend')
plt.show()

Available Endpoints

Endpoint	Description	Example
tokens	Information about all supported tokens	client.tokens.get(symbol="BTC,ETH")
hourly_ohlcv	Hourly price and volume data	client.hourly_ohlcv.get(symbol="BTC", startDate="2023-10-01", endDate="2023-10-10")
investor_grades	Long-term investment ratings	client.investor_grades.get(symbol="BTC,ETH", startDate="2023-10-01", endDate="2023-10-10")
trader_grades	Short-term trading signals	client.trader_grades.get(symbol="BTC,ETH", startDate="2023-10-01", endDate="2023-10-10")
trader_indices	AI-generated trading portfolios	client.trader_indices.get(startDate="2023-10-01", endDate="2023-10-10")
market_metrics	Overall market sentiment data	client.market_metrics.get(startDate="2023-10-01", endDate="2023-10-10")
ai_agent	Interact with Token Metrics AI chatbot	client.ai_agent.ask("What is your Bitcoin forecast?")
ai_reports	AI-generated analysis reports	client.ai_reports.get(symbol="BTC,ETH")
trading_signals	AI-generated trading signals	client.trading_signals.get(symbol="BTC,ETH", startDate="2023-10-01", endDate="2023-10-10", signal="1")

Detailed Usage Examples

Working with Trading Signals

# Get bullish trading signals (signal=1) for Bitcoin
signals = client.trading_signals.get_dataframe(
    symbol="BTC", 
    startDate="2023-10-01", 
    endDate="2023-10-10",
    signal="1"  # 1=Bullish, -1=Bearish
)

# Calculate potential returns
print(f"Average signal return: {signals['TRADING_SIGNALS_RETURNS'].mean():.2f}%")
print(f"Average holding return: {signals['HOLDING_RETURNS'].mean():.2f}%")

Getting AI-Generated Reports

# Get comprehensive AI reports for Ethereum
eth_reports = client.ai_reports.get(symbol="ETH")

# Access specific report sections
trader_report = eth_reports['data'][0]['TRADER_REPORT']
tech_report = eth_reports['data'][0]['TECHNOLOGY_REPORT']
fundamental_report = eth_reports['data'][0]['FUNDAMENTAL_REPORT']

print(f"Trading Report Excerpt:\n{trader_report[:200]}...")

Analyzing Market Metrics

# Get market sentiment metrics
metrics = client.market_metrics.get_dataframe(
    startDate="2023-10-01", 
    endDate="2023-10-10"
)

# Analyze Fear & Greed Index trends
plt.figure(figsize=(12, 6))
metrics.plot(x='DATE', y='FEAR_AND_GREED_VALUE', figsize=(10, 6))
plt.title('Crypto Fear & Greed Index')
plt.axhline(y=50, color='r', linestyle='-', alpha=0.3)
plt.show()

Authentication

All API requests require an API key. You can get your API key by signing up at Token Metrics.

# Initialize with your API key
client = TokenMetricsClient(api_key="your-api-key")

Error Handling

The SDK provides built-in error handling for API requests:

try:
    data = client.tokens.get(symbol="INVALID_SYMBOL")
except Exception as e:
    print(f"Error: {e}")
    # Handle the error appropriately

Requirements

• Python 3.6+
• requests package
• pandas package

Documentation

For complete API documentation, visit:

• Token Metrics API Documentation
• SDK Examples

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This SDK is distributed under the MIT License. See the LICENSE file for details.

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Empowering investors with AI-powered crypto insights