Basics
Beginner
What Is Cryptocurrency? A No-Hype Explanation
What a blockchain actually is, what owning a coin means, the main asset categories — and what crypto is not. Plain English, no hype.
Practical guide: install and check CROT
Start with ownership and custody, continue through centralized exchanges and futures, then move into strategy design, algorithmic execution and backtesting. Each guide states a concrete learning outcome and focuses on decisions you can verify in data or exchange state.
The rule remains strict: no price predictions and no guaranteed edge. Education can make the mechanics, assumptions and failure modes clear; it cannot remove market risk.
Understand the ledger, ownership, custody and asset economics before thinking about a trade.
What a blockchain actually is, what owning a coin means, the main asset categories — and what crypto is not. Plain English, no hype.
How seed phrases, hardware wallets, multisig and exchange custody work — plus a threat-model checklist for deciding where to keep crypto.
A practical framework for reserves, depeg risk, supply schedules, unlocks, market cap, FDV and on-chain metrics — without mistaking activity for value.
Learn what a CEX holds for you, how the order book sets price and what execution really costs.
Order books, matching engines, maker/taker fees, custody, liquidation engines and APIs — what happens behind the buy button on a CEX.
Evaluate jurisdiction, custody, reserves, withdrawals, liquidity, fees, API controls and incident response before funding a centralized exchange.
How orders interact with the book, what spread, depth and slippage cost, and how to choose execution instructions for manual or algorithmic trading.
Global liquidity, real interest rates, ETF flows, leverage cycles and regulation — the real drivers of Bitcoin's price, and why prediction still fails.
Why altcoins mostly amplify Bitcoin's moves, how token unlocks and thin liquidity distort prices, and why most alts underperform BTC over a full cycle.
Move from contract mechanics to a repeatable sizing, margin and loss-control workflow.
How perpetual futures work: leverage, margin, funding rates, liquidation — with concrete numbers and the honest math of why leverage cuts both ways.
Turn a stop distance into position size, estimate liquidation and funding exposure, choose isolated or cross margin, and preflight every futures trade.
Position sizing, stop-losses, leverage math and drawdown arithmetic — the part of trading with a guaranteed effect, explained with numbers.
Compare the market regimes, failure modes and indicators behind common discretionary and systematic approaches.
Trend following, mean reversion, grid, DCA, arbitrage and market making — what each strategy bets on, when it bleeds, and why none has guaranteed returns.
EMA, RSI, MACD, ATR and volume tools explained — and the honest truth: indicators describe the past, they do not predict the future.
Turn a research idea into a testable, observable and operationally safe trading system — or judge one you did not build.
The full trading-bot pipeline: market data, signals, portfolio state, risk, execution, reconciliation, monitoring and fail-closed operations.
Build point-in-time tests with fees, funding, slippage and walk-forward validation; judge strategies by expectancy, drawdown and robustness, not win rate.
Freqtrade, Hummingbot, Jesse, OctoBot and Passivbot compared on licence, purpose and community size — plus what to check instead of GitHub stars.
How exchanges mirror a lead trader onto your account, why your result diverges from theirs, what a profit share really costs, and how to read a track record.