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Biturai Trading Wiki
The Biturai crypto encyclopedia: AI-assisted, data-informed, and continuously quality-audited.
Understanding Liquidation Cascades and Risk Protection
Liquidation cascades occur when a series of forced position closures in leveraged trading triggers further market movements, leading to more liquidations.
Funding Rate Risk in Perpetual Positions
Perpetual futures contracts allow traders to speculate on asset prices without an expiration date, but they introduce a unique cost known as the funding rate.
Effective Leverage vs. Nominal Leverage: Measuring True Risk in Crypto Trading
Understanding the difference between effective and nominal leverage is fundamental for accurate risk assessment in crypto trading.
Edge Ratio: Evaluating Trading Advantage with MFE and MAE
The Edge Ratio is a powerful metric that helps traders quantify the profitability and efficiency of their trading strategies.
Monitoring Margin Ratio: Managing Distance to Liquidation
Understanding and actively monitoring your margin ratio is fundamental for any trader utilizing leverage in cryptocurrency markets. This metric provides a clear indication of your position's health and its proximity to forced liquidation.
Maximum Adverse Excursion in Trade Management
Maximum Adverse Excursion (MAE) quantifies the largest unrealized loss a trading position experiences from its entry point until it closes or reverses.
Isolated Margin: Limiting Risk Per Position in Crypto Trading
Isolated margin is a method in margin trading that confines the capital and borrowed funds to a single trading position, isolating its risk from the rest of a trader's account.
Calculating Liquidation Price in Cross-Margin vs. Isolated-Margin
Margin trading modes, Isolated Margin and Cross Margin, handle liquidation differently.
Why Martingale Averaging Down is Ruinous in Crypto Trading
The Martingale strategy, which involves doubling down on losing trades, is a high-risk approach that can lead to catastrophic losses in crypto markets.
Anti-Martingale Position Sizing: Increasing on Wins, Decreasing on Losses
The Anti-Martingale strategy is a position-sizing method where traders increase their trade size after winning trades and reduce it after losing ones.
Drawdown Recovery Plan: Reducing Position Size After Losses
A drawdown recovery plan involves strategically reducing position sizes after experiencing losses to manage risk and facilitate a sustainable return to profitability.
Pyramiding with Profits: Scaling Positions Risk-Neutrally
Pyramiding with profits is a trading strategy where a trader adds to a winning position without increasing the initial capital at risk.
Scale-Out: Selling a Profitable Position Incrementally
Scaling out is a strategic approach where traders gradually sell portions of a profitable investment as its price rises.
Limiting Simultaneous Positions as a Risk Rule
Limiting the number of open trading positions simultaneously is a fundamental risk management strategy. This approach prevents overexposure to market fluctuations and helps preserve trading capital.
Correlation-Adjusted Portfolio Heat for Open Trades
Correlation-adjusted portfolio heat measures the total potential loss across all open trades, considering how asset prices move in relation to each other.
Limiting Total Open Risk in Crypto Trading
Maximales Portfolio-Heat refers to the aggregate potential loss across all open trading positions within a portfolio. Effectively managing this total exposure is crucial for capital preservation in volatile cryptocurrency markets.
Maximum Favorable Excursion for Exit Optimization
Maximum Favorable Excursion (MFE) measures the highest unrealized profit a trade achieves before it is closed.
Expected Shortfall and Value at Risk: A Comparative Analysis
Value at Risk quantifies the maximum potential loss within a given confidence level, acting as a critical threshold for risk exposure.
Value at Risk Limitations: Underestimating Tail Risks
Value at Risk (VaR) is a widely used metric for estimating potential financial losses in an investment portfolio over a specific period with a certain confidence level.
VaR-Backtesting: Validating Value-at-Risk Models
VaR-Backtesting evaluates the accuracy of a Value-at-Risk (VaR) model by comparing its predicted losses against actual portfolio losses over time.