how-to How to Use ME10011: Derivatives in Trading How to Use ME10011: Derivatives in Trading Derivatives data enables funding carry, basis trades, and ETF premium arbitrage. Strategy 1: Funding Rate Carry (No Leverage) def funding_carry(): """Capture funding yield with spot hedge.""" funding = requests.get(f"{MADJIK_API}/metrics/ME10011/rate/
how-to How to Use ME10010: Regulatory in Trading How to Use ME10010: Regulatory in Trading Regulatory actions move markets. Get early warning of enforcement. Strategy: Regulatory Risk Assessment def regulatory_risk(): """Assess regulatory environment.""" actions = requests.get(f"{MADJIK_API}/metrics/ME10010/actions/now", headers=HEADERS).json() sentiment = requests.get(
how-to How to Use ME10009: Whale Activity in Trading How to Use ME10009: Whale Activity in Trading Whale data reveals smart money movements and manipulation risk. Strategy: Whale Flow Signals def whale_signal(): """Track large holder movements.""" movements = requests.get(f"{MADJIK_API}/metrics/ME10009/movements/now", headers=HEADERS).json() smart_
how-to How to Use ME10008: DeFi Protocol in Trading How to Use ME10008: DeFi Protocol in Trading TVL and yield source analysis helps avoid unsustainable yields and contagion risks. Strategy: TVL Quality Assessment def tvl_quality(protocol): """Assess real TVL vs inflated metrics.""" tvl = requests.get(f"{MADJIK_API}/metrics/ME10008/tvl_
how-to How to Use ME10007: Security & Custody in Trading How to Use ME10007: Security & Custody in Trading Security metrics help avoid DeFi exploits and protocol risks. Strategy: Protocol Risk Screening def protocol_risk(protocol): """Screen protocols before depositing.""" audit = requests.get(f"{MADJIK_API}/metrics/ME10007/audit_score/now", headers=
how-to How to Use ME10006: Exchange Health in Trading How to Use ME10006: Exchange Health in Trading Exchange health data protects against counterparty risk. Know before the next FTX. Strategy: Exchange Risk Allocation def exchange_risk(): """Allocate capital based on exchange health.""" exchanges = ["binance", "coinbase", "kraken"] scores
how-to How to Use ME10005: Mining & Network in Trading How to Use ME10005: Mining & Network in Trading Mining data reveals miner stress and network security. Miner capitulation often marks cycle bottoms. Strategy: Miner Capitulation Detection def miner_stress(): """Detect miner capitulation for bottom signals.""" hashrate = requests.get(f"{MADJIK_API}/metrics/
how-to How to Use ME10004: Market Valuation in Trading How to Use ME10004: Market Valuation in Trading MVRV and realized cap identify cycle extremes. Time major entries and exits across BTC, ETFs, and MSTR. Strategy: MVRV Cycle Timing def mvrv_signal(): """Use MVRV to identify cycle position.""" mvrv = requests.get(f"{MADJIK_
how-to How to Use ME10003: Volume Analysis in Trading How to Use ME10003: Volume Analysis in Trading Volume metrics separate real liquidity from wash trading. Essential for confirming trends and sizing positions. Strategy 1: Wash Trade-Adjusted Sizing def adjusted_volume(): """Get real volume after filtering wash trades.""" volume = requests.get(f"{MADJIK_
how-to How to Use ME10002: Order Book Liquidity in Trading How to Use ME10002: Order Book Liquidity in Trading Order book data reveals true market depth vs spoofed orders. Essential for sizing positions, routing orders, and timing entries across spot, perpetuals, ETFs, and MSTR. Why Madjik's ME10002 Data is Unique * Real depth vs displayed depth - filters out
how-to How to Use ME10001: Stablecoin Peg Metrics in Trading How to Use ME10001: Stablecoin Peg Metrics in Trading Stablecoin peg data is foundational for every crypto trade. When USDT depegs, it ripples through BTC spot, perpetuals, MSTR stock, and Bitcoin ETFs simultaneously—but at different speeds and magnitudes. Understanding these dynamics creates alpha. Why Madjik's ME10001 Data
(Untitled) 💡 Hypothesis HY10064 OUR HYPOTHESIS ✅ = Mining concentrates where electricity is cheap or free - often stolen, subsidized, or politically unstable Bitcoin mining is pure electricity arbitrage. Miners locate where power is cheapest - subsidized hydropower, stolen electricity, or politically unstable jurisdictions. This creates geographic concentration risk that affects network security. Trading
(Untitled) 💡 Hypothesis HY10038 Black-Scholes assumes log-normal distribution. Crypto has extreme kurtosis, fat tails, and flash crashes. The model is fundamentally broken. Trading hypothesis What traders get wrong False assumption: "Black-Scholes log-normal distribution applies." Truth: Crypto has extreme kurtosis (10-20 vs 3 normal), fat tails, and flash crashes that make
test h-59 💡 Hypothesis HY10059 OUR HYPOTHESIS ✅ = Criminal wallets are public - we can predict their behavior and market impact Every crypto transaction is public forever. Known criminal wallets, hacked funds, and sanctioned addresses are tagged and tracked. When these holdings move toward exchanges, we can predict the selling pressure before it hits.
test h64 💡 Hypothesis HY10064 OUR HYPOTHESIS ✅ = Mining concentrates where electricity is cheap or free - often stolen, subsidized, or politically unstable Bitcoin mining is pure electricity arbitrage. Miners locate where power is cheapest - subsidized hydropower, stolen electricity, or politically unstable jurisdictions. This creates geographic concentration risk that affects network security. Trading
test 13 💡 Hypothesis HY10067 FALSE ASSUMPTION: 🚫 "Crypto exchanges are like stock exchanges" → ✅ FACT/Hypothesis: Crypto is unregulated - exchanges trade against customers, print money from thin air, and lock exits Stock exchanges operate under strict regulations. Crypto exchanges operate in regulatory grey zones where they can trade against their own
test HYPO paste 12 💡 Hypothesis HY10049 BE AWARE ⚠️: Futures let you go long AND short - but the house sees all positions and hunts liquidations Crypto futures allow leveraged long and short positions. Unlike spot trading, futures create a zero-sum game where your counterparty profits from your loss. Exchanges can see all positions, all
test post-10 💡 Hypothesis HY10064 OUR HYPOTHESIS ✅ = Mining concentrates where electricity is cheap or free - often stolen, subsidized, or politically unstable Bitcoin mining is pure electricity arbitrage. Miners locate where power is cheapest - subsidized hydropower, stolen electricity, or politically unstable jurisdictions. This creates geographic concentration risk that affects network security. Trading
test copy USE 1 How to Use ME10001: Stablecoin Peg Metrics in Trading Stablecoin peg data is foundational for every crypto trade. When USDT depegs, it ripples through BTC spot, perpetuals, MSTR stock, and Bitcoin ETFs simultaneously—but at different speeds and magnitudes. Understanding these dynamics creates alpha. Why Madjik's ME10001 Data
test HYPO copy-paste-9 💡 Hypothesis HY10065 OUR HYPOTHESIS ✅ = Social media moves crypto more than insider knowledge - public discussion beats private whispers In traditional markets, insiders with private information have the edge. In crypto, public social media discussion often moves prices more than any private knowledge. The narrative is visible to everyone - the
test copy-paste-8 💡 Hypothesis HY10067 FALSE ASSUMPTION: 🚫 "Crypto exchanges are like stock exchanges" → ✅ FACT/Hypothesis: Crypto is unregulated - exchanges trade against customers, print money from thin air, and lock exits Stock exchanges operate under strict regulations. Crypto exchanges operate in regulatory grey zones where they can trade against their own
test copy-paste-7 💡 Hypothesis HY10008 DeFi TVL reached "$200 billion" at peak. But TVL is massively double-counted. Deposit ETH, get aETH, deposit aETH elsewhere - same ETH counted 2-3x. Trading hypothesis What traders get wrong False assumption: "TVL represents real capital deployed in DeFi." Truth: TVL is inflated through
test copy-paste-6 💡 Hypothesis HY10006 Bitcoin is supposed to be decentralized. But 3 mining pools control over 50% of hash rate. They could theoretically censor transactions or collude against users. Trading hypothesis What traders get wrong False assumption: "Bitcoin is decentralized. No single entity controls the network." Truth: Mining is highly
test copy-paste-2 Madjik Data available: ✅ Yes Get API access now: https://www.madjik.io/pricing/ Madjik Data available: ✅ Yes Get API access now: https://www.madjik.io/pricing/ Available metrics for this hypothesis: Metric Description ME10013 Order book authenticity score API spec for ME10013 ME10014 Cancel rate analysis API spec for ME10014
Test copy-paste 💡 Hypothesis HY10019 Every trading model that looks great in backtests fails in live trading. Past performance doesn't predict future results - especially in crypto where market structure changes constantly. Trading hypothesis What traders get wrong False assumption: "My model has great backtested returns." Truth: Back-tested models
