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

Normalize consistently - Madjik metrics are 0-100, but double-check
Handle missing data - Use forward-fill or interpolation
Feature selection - Start with correlated metrics, prune redundant
Cross-validation - Crypto regimes shift; use time-series CV
Retrain regularly - Market structure changes

Good night