Custom Indicators

Custom Crypto Indicators

Why Standard TA Falls Short for Crypto

Traditional technical analysis was built for equities and forex — markets with fixed supply, regulated exchanges, and institutional-dominated order flow. Crypto markets have unique properties that demand purpose-built indicators:

• On-chain transparency: Every transaction is public. We can measure real economic activity, not just price and volume on a single exchange.
• Supply mechanics: Fixed or programmatic supply schedules make supply-side analysis (velocity, holder distribution) meaningful.
• Derivatives dominance: Perpetual futures funding rates and open interest often drive spot price, not the other way around.
• Whale concentration: A small number of wallets hold outsized supply. Tracking their behavior provides alpha that equity-market TA cannot.
• Exchange flows: On-chain deposit/withdrawal to centralized exchanges signals intent to sell or accumulate.

This skill covers nine crypto-native indicators. Each section includes the formula, interpretation guide, data sources, and a working code snippet.

Files

Indicator 1: NVT Ratio

Network Value to Transactions — the crypto equivalent of a P/E ratio.

NVT = Market Cap / Daily On-Chain Transaction Volume (USD)

• High NVT (> 65): Network is overvalued relative to its economic throughput. Bearish signal.
• Low NVT (< 25): Network is undervalued or seeing heavy real usage. Bullish signal.
• Data sources: CoinGecko (market cap), blockchain explorers or DeFiLlama (transaction volume).

def nvt_ratio(market_cap: float, daily_tx_volume_usd: float) -> float:
    """Compute NVT ratio.

    Args:
        market_cap: Current market capitalization in USD.
        daily_tx_volume_usd: 24h on-chain transaction volume in USD.

    Returns:
        NVT ratio value.
    """
    if daily_tx_volume_usd <= 0:
        return float("inf")
    return market_cap / daily_tx_volume_usd

Smoothing: Apply a 14-day or 28-day moving average to NVT (called NVT Signal) to reduce noise from daily volume spikes.

Indicator 2: MVRV Ratio

Market Value to Realized Value — compares the current market cap to the aggregate cost basis of all holders.

MVRV = Market Cap / Realized Cap
Realized Cap = Sum of (each UTXO * price when it last moved)

• MVRV > 3.5: Most holders are in deep profit. Distribution likely.
• MVRV < 1.0: Most holders are underwater. Historically marks bottoms.
• Data sources: Glassnode, CryptoQuant (Bitcoin/Ethereum). For Solana tokens, approximate via average entry price of top holders.

def mvrv_ratio(market_cap: float, realized_cap: float) -> float:
    """Compute MVRV ratio.

    Args:
        market_cap: Current market capitalization in USD.
        realized_cap: Realized capitalization (aggregate cost basis).

    Returns:
        MVRV ratio value.
    """
    if realized_cap <= 0:
        return float("inf")
    return market_cap / realized_cap

For tokens without UTXO-based realized cap, estimate using average purchase price from DEX trade history multiplied by circulating supply.

Indicator 3: Exchange Flow

Net exchange deposits minus withdrawals — signals selling or accumulation intent.

Exchange Netflow = Deposits to Exchanges - Withdrawals from Exchanges

• Positive netflow (large deposits): Holders moving tokens to exchanges, likely to sell. Bearish.
• Negative netflow (withdrawals): Tokens leaving exchanges to cold storage. Bullish accumulation signal.
• Data sources: CryptoQuant, Glassnode. For Solana SPL tokens, track transfers to known exchange wallets via Helius or Solana RPC.

def exchange_netflow(
    deposits_usd: float, withdrawals_usd: float
) -> tuple[float, str]:
    """Compute exchange netflow and interpret.

    Returns:
        Tuple of (netflow_value, signal_label).
    """
    netflow = deposits_usd - withdrawals_usd
    if netflow > 0:
        signal = "bearish"
    elif netflow < 0:
        signal = "bullish"
    else:
        signal = "neutral"
    return netflow, signal

Normalize by market cap for cross-token comparison: Netflow Ratio = Netflow / Market Cap.

Indicator 4: Funding Rate Signal

Perpetual futures contracts use funding rates to anchor price to spot.

Funding Rate = (Perp Mark Price - Spot Price) / Spot Price
             (paid every 8 hours on most exchanges)

• Highly positive (> 0.05%): Longs pay shorts. Market is overleveraged long. Contrarian bearish.
• Highly negative (< -0.05%): Shorts pay longs. Overleveraged short. Contrarian bullish.
• Data sources: Binance, Bybit, dYdX APIs. Aggregate across exchanges for a volume-weighted average.

def funding_rate_signal(
    rates: list[float], weights: list[float] | None = None
) -> tuple[float, str]:
    """Volume-weighted average funding rate with signal.

    Args:
        rates: Funding rates from multiple exchanges.
        weights: Optional volume weights per exchange.
    """
    import numpy as np

    if weights is None:
        weights = [1.0 / len(rates)] * len(rates)
    vw_rate = float(np.average(rates, weights=weights))
    if vw_rate > 0.0005:
        signal = "bearish"
    elif vw_rate < -0.0005:
        signal = "bullish"
    else:
        signal = "neutral"
    return vw_rate, signal

Indicator 5: Open Interest Momentum

Tracks the rate of change in total open interest across derivatives exchanges.

OI Momentum = (OI_today - OI_n_days_ago) / OI_n_days_ago * 100

• Rising OI + Rising Price: New money entering longs. Trend confirmation.
• Rising OI + Falling Price: New shorts opening. Bearish pressure.
• Falling OI + Rising Price: Short squeeze / closing shorts.
• Falling OI + Falling Price: Long liquidation.
• Data sources: CoinGlass, Binance, Bybit open interest endpoints.

def oi_momentum(
    oi_series: list[float], lookback: int = 7
) -> float:
    """Compute open interest momentum as percentage change.

    Args:
        oi_series: Daily open interest values (newest last).
        lookback: Number of days for momentum calculation.
    """
    if len(oi_series) < lookback + 1:
        return 0.0
    old = oi_series[-(lookback + 1)]
    new = oi_series[-1]
    if old <= 0:
        return 0.0
    return (new - old) / old * 100.0

Indicator 6: Holder Momentum

Tracks the net change in unique token holders over time.

Holder Momentum = (Holders_today - Holders_n_days_ago) / Holders_n_days_ago
Holder Acceleration = Holder Momentum_today - Holder Momentum_yesterday

• Accelerating growth: Viral adoption phase. Bullish.
• Decelerating growth: Adoption slowing. Watch for reversal.
• Negative momentum: Holders leaving. Bearish.
• Data sources: Helius DAS API (Solana), Etherscan token holder count, Birdeye holder stats.

def holder_momentum(
    holder_counts: list[int], lookback: int = 7
) -> tuple[float, float]:
    """Compute holder momentum and acceleration.

    Returns:
        Tuple of (momentum_pct, acceleration).
    """
    if len(holder_counts) < lookback + 2:
        return 0.0, 0.0
    old = holder_counts[-(lookback + 1)]
    new = holder_counts[-1]
    prev_old = holder_counts[-(lookback + 2)]
    prev_new = holder_counts[-2]
    mom = (new - old) / old if old > 0 else 0.0
    prev_mom = (prev_new - prev_old) / prev_old if prev_old > 0 else 0.0