🔬Phasemap Algorithms

Phasemap’s AI consists of interconnected logic modules, each tracking specific market aspects — from phase shifts to volatility forecasting. Below are simplified prototypes in both Python and JavaScript, demonstrating core ideas that power our AI-driven insights.

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1. PhaseMap — Cycle Transition Detector

📘 Language: Python

def phase_map(market_data):
    phase_threshold = 0.1
    price_change = (market_data["current_price"] - market_data["previous_price"]) / market_data["previous_price"]
    volume_change = (market_data["current_volume"] - market_data["previous_volume"]) / market_data["previous_volume"]

    if abs(price_change) > phase_threshold or abs(volume_change) > phase_threshold:
        return "Alert: Market Phase Transition Detected"
    else:
        return "Market Phase Stable"

AI Logic: Detects market phase transitions—such as from accumulation to breakout—by monitoring significant price and volume changes.

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2. GrowthShift — Trend Momentum Forecaster

📘 Language: Python

def growth_shift(market_data):
    price_growth = (market_data["current_price"] - market_data["previous_price"]) / market_data["previous_price"]
    depth_factor = market_data["total_volume"] / market_data["market_liquidity"]
    
    prediction_score = price_growth * depth_factor

    if prediction_score > 0.1:
        return "Alert: Market Growth Predicted"
    else:
        return "Market Stable"

AI Logic: Models potential market growth by combining price momentum with market depth indicators.

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3. DataWave — Real-Time Volatility Scanner

📘 Language: JavaScript

function dataWave(marketData) {
  const volatilityIndex = marketData.priceChange / marketData.previousPrice;
  const liquidityRisk = marketData.totalVolume / marketData.marketLiquidity;

  const marketRisk = volatilityIndex * liquidityRisk;

  if (marketRisk > 0.5) {
    return 'Alert: High Market Volatility Detected';
  } else {
    return 'Market Volatility Low';
  }
}

AI Logic: Detects real-time market turbulence by measuring price fluctuations relative to liquidity conditions.

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4. MarketLens — Phase Visual Interpreter

📘 Language: Python

def market_lens(market_data):
    price_change_pct = (market_data["current_price"] - market_data["previous_price"]) / market_data["previous_price"]
    volatility_impact = market_data["price_fluctuation"] / market_data["volume"]

    if price_change_pct > 0.2 and volatility_impact > 0.3:
        return "Alert: Market Growth Phase Detected"
    elif price_change_pct < -0.2 and volatility_impact > 0.3:
        return "Alert: Market Decline Phase Detected"
    else:
        return "Market in Neutral Phase"

AI Logic: Provides a visual phase label based on price direction and volatility impact, helping users grasp current market mood.

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5. TrendGuard — Market Risk Detector

📘 Language: Python

def trend_guard(market_data):
    trend_score = (market_data["current_price"] - market_data["previous_price"]) / market_data["previous_price"]
    volume_score = market_data["volume"] / market_data["previous_volume"]

    risk_score = abs(trend_score) * volume_score

    if risk_score > 0.15:
        return "Alert: High Market Risk Detected"
    else:
        return "Market Trend Stable"

AI Logic: Flags potential market instability by combining aggressive price movements with abnormal volume surges.