Using Madjik API for ML

Build and train machine learning models using Madjik's comprehensive crypto market data.

Overview

Madjik API provides ML-ready data:

• Historical time series for model training

• Normalized features (0-100 scales) requiring minimal preprocessing

• Multiple timeframes (1min, 5min, 1hr, 1day)

• Rich feature set across 44 metrics

ML Use Cases

1. Price Prediction Models

import pandas as pd
from sklearn.ensemble import RandomForestRegressor

# Fetch historical features
def get_training_data(days=90):
    features = ["ME10014", "ME10015", "ME10016", "ME10030"]
    data = []
    
    for metric in features:
        resp = requests.get(
            f"https://api.madjik.io/v1/metrics/{metric.lower()}/history",
            params={"days": days},
            headers={"Authorization": "Bearer YOUR_API_KEY"}
        )
        data.append(pd.DataFrame(resp.json()['data']))
    
    return pd.concat(data, axis=1)

# Train model
X = get_training_data()
y = get_price_returns()  # Your price data
model = RandomForestRegressor()
model.fit(X, y)

2. Regime Classification

from sklearn.cluster import KMeans

# Cluster market regimes
def classify_regime():
    features = requests.get(
        "https://api.madjik.io/v1/metrics/batch",
        params={"metrics": "ME10018,ME10020,ME10023"},
        headers={"Authorization": "Bearer YOUR_API_KEY"}
    ).json()
    
    # K-means clustering
    kmeans = KMeans(n_clusters=4)  # Bull, Bear, Accumulation, Distribution
    regime = kmeans.predict([[
        features['ME10018']['value'],
        features['ME10020']['value'],
        features['ME10023']['value']
    ]])
    return regime

3. Anomaly Detection

from sklearn.ensemble import IsolationForest

# Detect unusual market conditions
def detect_anomalies():
    metrics = ["ME10007", "ME10035", "ME10006"]  # Volume, Tether comms, Manipulation
    
    data = []
    for m in metrics:
        resp = requests.get(
            f"https://api.madjik.io/v1/metrics/{m.lower()}/history",
            params={"hours": 24},
            headers={"Authorization": "Bearer YOUR_API_KEY"}
        )
        data.append(resp.json()['data'])
    
    clf = IsolationForest(contamination=0.1)
    anomalies = clf.fit_predict(data)
    return anomalies

Feature Engineering Tips

Madjik Metric	ML Feature Ideas
ME10014 (Funding)	Rolling mean, z-score, regime changes
ME10016 (Liquidation)	Threshold crossings, acceleration
ME10030 (Sentiment)	Delta, momentum, divergence from price
ME10010 (Whale)	Flow direction, accumulation streaks

Best Practices

1. Normalize consistently - Madjik metrics are 0-100, but double-check

2. Handle missing data - Use forward-fill or interpolation

3. Feature selection - Start with correlated metrics, prune redundant

4. Cross-validation - Crypto regimes shift; use time-series CV

5. Retrain regularly - Market structure changes

See Also

• AI Integration Guide

• MCP Integration Guide

• All Metrics