Trading Strategies

Shares practical strategies, techniques, and risk management methods. By combining market case studies with technical analysis, it helps traders optimize decision-making and enhance profitability.

When the Spreads Reach 50%: The Rise of Pre-Market Crypto-Stock Arbitrage

In the context of the crypto bear market, a new arbitrage opportunity is gaining traction: pre-IPO stock tokenization trading across different platforms. The article highlights significant price discrepancies—sometimes exceeding 50%—for high-profile companies like Kalshi, Polymarket, and SpaceX (xAI) on platforms such as PreStocks, Jarsy, and Tessera. These gaps, driven by varying trading mechanisms and liquidity conditions, reveal a strong market demand for a unified platform that bridges traditional and crypto pre-market trading. The piece argues that building such an intermediary could capitalize on these arbitrage opportunities, especially with a wave of major IPOs expected in 2026, while generating revenue through trading fees and liquidity provision.

比推03/16 04:36

When the Spreads Reach 50%: The Rise of Pre-Market Crypto-Stock Arbitrage

比推03/16 04:36

Racing 22 Agents on Hyperliquid: What Did I Discover? (Full Strategy Code Included)

Summary: We deployed 22 autonomous AI trading agents on Hyperliquid, each with $1000, executing over 5000 trades. Key findings: 1) Less trading with higher conviction yields better results—agents with under 120 trades were profitable, while those with over 400 lost significantly. 2) Profits follow a power law: 3-5 trades generated nearly all gains, with small losses quickly stopped out. 3) Real-time "Hyperfeed" data (tracking where smart money is profiting) outperformed pure technical analysis. 4) Mean-reversion strategies failed in perpetual markets; trend-following was more effective. 5) Agents self-adjusted poorly when losing, often accelerating losses by increasing leverage or removing safeguards. Future steps include hardening risk controls in code and testing new strategies. All code is open-sourced, and experiments continue live.

Odaily星球日报03/16 03:40

Racing 22 Agents on Hyperliquid: What Did I Discover? (Full Strategy Code Included)

Odaily星球日报03/16 03:40

Understanding Theory ≠ Gaining Profit: 5 Common Math Mistakes Made by Highly Intelligent People

In the article "Knowing Theory ≠ Earning Returns: 5 Common Math Mistakes Made by Highly Intelligent People," crypto KOL darkzodchi explores why many highly educated individuals struggle financially despite their intellectual prowess, while less academically trained traders often succeed. The author identifies five key cognitive errors: 1. **Pursuing Precision Over Action**: Smart people often delay decisions to seek perfect accuracy, underestimating the cost of delay. The solution is to set deadlines and prioritize timely action over exhaustive research. 2. **Finding Patterns in Noise**: Intelligent individuals tend to overfit models by detecting false patterns in random data. The remedy is to apply statistical corrections (e.g., Bonferroni) and avoid complex strategies prone to noise. 3. **Misapplying Diversification**: Diversification is useful without an edge but harmful when one has a genuine advantage. The Kelly Criterion suggests concentrating bets based on the strength of the edge. 4. **Anchoring to Irrelevant Numbers**: People often fixate on past prices or values, impairing rational decision-making. Asking "Would I buy this today?" helps ignore sunk costs. 5. **Confusing Understanding with Action**: Knowledge alone doesn’t yield results; action and consistency are crucial. Small, real-world bets bridge the gap between theory and practice. The author emphasizes that markets reward simplicity, speed, and execution over complexity and perfection. Intelligent individuals must adapt by embracing practical action rather than endless analysis.

marsbit03/14 14:58

Understanding Theory ≠ Gaining Profit: 5 Common Math Mistakes Made by Highly Intelligent People

marsbit03/14 14:58

Your Backtest Is Lying: Why You Must Use Point-in-Time Data

The article warns that backtests using revised historical data are misleading due to look-ahead bias. It illustrates this with a hypothetical trading strategy based on Bitcoin exchange outflows from Binance: entering the market when the 5-day moving average of BTC balance falls below the 14-day average, and exiting on the reverse. Initial backtest results using current data appeared reasonably successful, even comparable to buy-and-hold at times. However, the author argues this is deceptive because metrics like exchange balances are often revised retroactively as new data arrives, meaning a backtest uses information that wasn't available in real-time. Re-running the identical strategy using Point-in-Time (PiT) data—which is immutable and reflects only what was known when the data was first published—yielded significantly worse performance. The PiT-based strategy failed to capture key market upswings as effectively, resulting in meaningfully lower cumulative returns. The key takeaway is that only immutable, Point-in-Time data ensures a backtest accurately replays history without look-ahead bias, and that using revised data will produce lying backtests.

insights.glassnode03/13 14:12

Your Backtest Is Lying: Why You Must Use Point-in-Time Data

This article warns against a common pitfall in backtesting trading strategies: look-ahead bias caused by using revised historical data. It illustrates this with a hypothetical Bitcoin strategy based on exchange outflows from Binance. The strategy is built on the premise that sustained outflows (when the 5-day moving average of BTC balance falls below the 14-day average) are bullish, while inflows signal a sell-off. An initial backtest using standard, revised data shows the strategy performing comparably to a simple buy-and-hold approach. However, the author argues these results are misleading because the data has been updated with information that wasn't available in real-time. This data mutation creates an unfair advantage in the backtest. To demonstrate, the test is rerun using Point-in-Time (PiT) data—an immutable, append-only record that reflects only what was known on any given day. The results are significantly worse, as the PiT-based strategy misses key profitable moves. The key takeaway is that accurate backtesting requires immutable Point-in-Time data to avoid look-ahead bias and replay history honestly.

insights.glassnode03/13 14:12

Polymarket Arbitrage Bible: The Real Gap Lies in Mathematical Infrastructure

This article explores the mathematical infrastructure behind profitable arbitrage strategies on Polymarket, emphasizing that the key advantage lies not in speed alone but in advanced computational techniques. While manual traders might identify simple arbitrage opportunities (e.g., buying both YES and NO shares when their sum is less than $1), quantitative systems use integer programming to analyze complex logical dependencies across thousands of markets, avoiding brute-force enumeration of exponentially many outcomes. The core method involves: - Modeling price constraints via marginal polytopes. - Using Bregman projections (KL divergence) to compute optimal arbitrage trades, respecting the information-sensitive nature of probability-based pricing in LMSR (Logarithmic Market Scoring Rule) mechanisms. - Applying the Frank-Wolfe algorithm with integer programming solvers like Gurobi to efficiently approximate solutions without full enumeration. Execution challenges include non-atomic order execution in CLOBs, liquidity constraints, and VWAP-based slippage estimation. Real-world data shows $39.7M in extracted profits over one year, with the top arbitrageur earning over $2M. The gap between casual and professional trading is attributed to sophisticated math infrastructure, not luck.

marsbit03/11 05:09

Polymarket Arbitrage Bible: The Real Gap Lies in Mathematical Infrastructure

marsbit03/11 05:09

MEXC Lunar Copy Trade Festival Concludes with Over 7,600 Registrations and $15M in Copy Trading Volume

MEXC concluded its "Lunar Copy Trade Festival" on February 25, 2026, attracting over 7,600 registrations and generating approximately $15 million in copy trading volume. The two-week event featured incentives for all user levels, including loss protection for new traders and tiered rewards through an AI Copy Trading Challenge. Central to the festival was MEXC's AI copy trading feature, which connected users with eight AI-Masters using diverse strategies on major cryptocurrencies and commodities. These AI traders operate with a 1% profit share, significantly lower than typical human traders, aiming to make active trading more accessible. MEXC plans to expand its AI copy trading services and host more user-focused events in the future.

TheNewsCrypto03/10 10:18

MEXC Lunar Copy Trade Festival Concludes with Over 7,600 Registrations and $15M in Copy Trading Volume

TheNewsCrypto03/10 10:18

Dissecting 112,000 Polymarket Addresses: The Top 1% Who Truly Profit Are All Doing These Five Things

By analyzing 112,000 Polymarket wallets over six months, this study reveals that 87.3% of users lose money. After filtering for statistically significant accounts (8,400 wallets with ≥100 settled positions, >4 months activity, >$10k volume, and participation in ≥2 markets), key patterns among the top 1% of profitable traders emerge: 1. They trade against extreme sentiment, entering when market odds deviate 6-11% from their calculated probabilities, exploiting the "favorite-longshot bias." 2. They use position sizing aligned with a conservative quarter-Kelly Criterion, betting larger only with clear edges. 3. They specialize in 1-2 market categories (e.g., only crypto or weather), avoiding diversification across unrelated fields. 4. They trade price movements, not outcomes, often exiting before settlement after capturing mispricings, with average holding periods of 18-72 hours. 5. They avoid trading during breaking news, waiting for emotional reactions to subside before entering. Common misconceptions include overestimating win rates (top traders average 55-67%, not 80-90%), believing diversification improves results, and overvaluing speed. The top performers focus on process: specializing, recording predictions, sizing positions cautiously, trading only with high edges, and systematically reviewing their trades.

Odaily星球日报03/09 04:05

Dissecting 112,000 Polymarket Addresses: The Top 1% Who Truly Profit Are All Doing These Five Things

Odaily星球日报03/09 04:05

Why Only Dollar-Cost Averaging Can Capture Bitcoin's Long-Term Dividends?

This article argues that dollar-cost averaging (DCA) is the optimal strategy for capturing Bitcoin's long-term gains, supported by historical backtesting and forward-looking models. The strategy involves investing a fixed amount regularly, regardless of market conditions. A five-year DCA simulation, starting in January 2021 with weekly $250 investments, resulted in a total investment of $67,500, acquiring approximately 1.65 BTC at an average price of $40,884. At a price of $71,000, the portfolio was worth ~$120,500, a 76% gain. Projections show even higher returns at $100,000 and the expected 2025 cycle peak of ~$126,000. A shorter-term DCA example starting in January 2024 showed an initial paper loss at current prices but significant projected profits at higher future price points. A comparison over five years also demonstrated that DCA into Bitcoin outperformed DCA into the S&P 500. Long-term modeling, based on Bitcoin's power-law growth curve, projects a median price of around $430,000 by 2030. A four-year DCA plan starting in 2026 could accumulate roughly 0.30 BTC, leading to substantial returns even under conservative price estimates. The key conclusion is that while entry timing affects the magnitude of gains, the duration of the holding period is the most critical factor for maximizing returns.

比推03/06 15:05

Why Only Dollar-Cost Averaging Can Capture Bitcoin's Long-Term Dividends?

比推03/06 15:05

Bear Market Script: Which Act Is Your 'Faith' Experiencing?

The article "Bear Market Script: Which Act Is Your 'Faith' Experiencing?" by TVBee analyzes the typical stages of a cryptocurrency bear market, using Bitcoin (BTC) and Tether (USDT) market capitalization as key indicators. It identifies 3-4 phases: 1. **Reaction Phase (Faith Intact)**: BTC declines while USDT rises, indicating some investors still hold hope. 2. **Confirmation Phase (Faith Collapses)**: BTC may fall or stagnate, and USDT decreases, confirming the bear market as capital exits. 3. **Accumulation Phase (Faith Consolidates)**: BTC may drop or trade sideways, but USDT rises again, suggesting stronger believers are preparing to re-enter. 4. **Final Panic Phase (Black Swan)**: A optional phase where both BTC and USDT fall sharply due to extreme events (e.g., 2022’s Luna collapse). The author suggests the current market (early 2026) is likely in Phase 2, driven by geopolitical tensions like the Iran conflict. The duration of this phase depends on external factors such as Trump administration policies, monetary changes, and potential black swan events. Ideal entry points for investors are during Phase 3 (USDT rising) or in the recovery phase (right-side entry), where USDT growth and BTC stabilization signal a market rebound. The analysis notes that bear markets are evolving—Phase 1 is shortening, but Phase 2 remains unpredictable due to external shocks. Caution and patience are advised.

marsbit03/05 07:45