Event-driven trading capitalises on price dislocations caused by scheduled and unscheduled catalysts — earnings announcements, FOMC meetings, governance votes, protocol upgrades, token unlocks, mergers, and geopolitical shocks. The edge comes from correctly anticipating the event's impact or exploiting the volatility expansion around it.
Implied vs realised volatility around events. Earnings surprises and guidance revisions. Token unlock schedules and supply shocks. Protocol upgrades and hard forks. Mergers, acquisitions, and spin-offs. Regulatory announcements and legal rulings.
Pre-event: straddle/strangle if volatility is cheap relative to history. Directional: position before event if strong thesis. Post-event: trade the fade or continuation after initial reaction settles into clear structure.
Volatility crush after event (close straddles). Initial reaction reversed significantly. Target hit or thesis invalidated by the actual event outcome.
15-minute to 4H for intraday events (FOMC, earnings). Daily for multi-day events (token unlocks). Weekly for structural events (hard forks, regulatory).
Never risk your entire account on a single event. Use options or small position sizes to limit binary risk. The second move after an event (the 're-think') often provides better setups than the initial spike.
Value investing identifies assets trading below their intrinsic or fundamental value and holds until the market recognises the discount. In traditional markets this means studying P/E ratios, discounted cash flows, and book value; in crypto it extends to token economics, revenue-generating protocols, and fully diluted valuation analysis.
Earnings and valuation analysis evaluates a company's financial performance and market pricing to determine whether its stock is undervalued, fairly valued, or overvalued. By examining revenue growth, profit margins, earnings per share, and valuation multiples such as price-to-earnings and price-to-sales ratios, traders and investors can assess whether the current market price is justified by fundamentals.
Qualitative factor analysis evaluates the non-numerical attributes of an asset or company that influence long-term value — management quality, competitive moats, brand strength, regulatory positioning, and industry trends. While quantitative metrics measure what has happened, qualitative analysis assesses the strategic factors that will drive future performance, providing context that numbers alone cannot capture.
Macro trading uses macroeconomic data — GDP, inflation, interest rates, employment, and central bank policy — to establish directional biases across currencies, equities, bonds, and commodities. Traders position for large economic regime shifts rather than intraday noise, using fundamental catalysts as the primary edge.