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.

From 'Scientist' to 'Prophet': How Did OpenClaw Help Him Earn $100,000 in 10 Days?

This article features an interview with Kevin, a trader who used OpenClaw to turn $30,000 into over $100,000 in just 10 days on the prediction market platform Polymarket. Originally an ERP architect and later a developer of sports betting systems, Kevin entered the crypto space in 2018. He made his first significant profit during the 2023 "inscription summer" with ORDI. In summer 2025, he shifted to Polymarket, attracted by its improved liquidity and user experience. Initially, he used automated arbitrage strategies between betting odds, doubling his initial $100,000 investment. Since late February, he integrated OpenClaw into his strategy. While 60% of his funds still run automated arbitrage, 40% are now managed with OpenClaw-assisted subjective betting. OpenClaw helps by gathering and weighting factors like smart money movements, public sentiment, and team lineups—even uncovering new influencing factors. It also automates backtesting and execution. Kevin highlights Polymarket’s AI-friendly API as a key advantage. Currently, OpenClaw is experimenting mainly in sports betting with small automated bets (~$1,000). Kevin plans to develop and sell packaged OpenClaw "Skills" based on his strategy in the future.

比推03/16 13:13

From 'Scientist' to 'Prophet': How Did OpenClaw Help Him Earn $100,000 in 10 Days?

比推03/16 13:13

If You Bought One Deep OTM Bitcoin Put Option Every Month Since 2018, Could You Make Money in the Long Run?

Based on a systematic backtest from 2018 to 2026, this study examines the long-term profitability of a monthly strategy of buying one deep out-of-the-money (OTM) put option on Bitcoin (BTC) and Ethereum (ETH), with a target delta of 0.01 and a 30-day expiration. The results are highly divergent. The strategy is not a stable source of profit but a classic, path-dependent tail insurance tool characterized by extreme right skew, very low win rates, and severe drawdowns. For BTC, the strategy yielded a final total return of 97.62% (CAGR: 8.66%), while for ETH, it resulted in a -73.07% loss (CAGR: -14.78%). The performance difference is attributed to BTC's extreme payouts being sufficient to cover the long-term cost of premiums, whereas ETH's were not. Key characteristics of the strategy include: * Extremely low win rates (BTC: 2.04%, ETH: 1.02%). * Catastrophic maximum drawdowns (BTC: -97.24%, ETH: -93.82%). * The median trade return was -100% for both assets. * Profits are driven entirely by a few extreme winning trades, with the top 5 trades contributing over 10x the net profit for BTC. * Notably, not all major market crashes (e.g., March 2020, LUNA, FTX) resulted in profitable positions due to timing and strike price placement. Parameter sensitivity analysis showed that a delta of 0.02 offered a more balanced risk-return profile across metrics. The strategy is best suited for investors who can tolerate years of continuous losses, view it as portfolio insurance rather than a primary alpha generator, and seek convexity against extreme downside events. It is not suitable for those seeking stable returns or with low risk tolerance.

marsbit03/16 11:11

If You Bought One Deep OTM Bitcoin Put Option Every Month Since 2018, Could You Make Money in the Long Run?

marsbit03/16 11:11

Post-Mortem of the Venus THE Attack: How to Profit in a Fleeting Window?

Approximately two hours ago, Venus Protocol's THE token was exploited using a classic Mango Markets-style price manipulation attack. The attacker targeted THE, a low-liquidity collateral asset, by depositing it, borrowing other assets, and using those to buy more THE, artificially inflating its price. Once the time-weighted average oracle updated, the inflated price allowed further leveraged borrowing. To bypass THE's borrowing cap, the attacker performed a "donation attack" by transferring THE directly to the vTHE contract, increasing the recognized collateral value. After the first manipulation phase, THE's price stabilized around $0.50. The attacker attempted to further amplify gains by continuing to buy THE, but mounting sell pressure limited price increases and pushed their health factor near 1.0, risking liquidation. The collateral, nominally valued around $30M, had extremely low liquidity, making large-scale liquidation at inflated prices impossible. Recognizing the situation, the writer opened a short position on THE with high leverage, anticipating a price collapse due to overvaluation, illiquidity, and forced selling. After liquidation, THE price plummeted to ~$0.24, below its pre-attack level, resulting in a ~$15K profit for the writer. Venus Protocol was left with ~$2M in bad debt. The attacker likely gained little or lost funds, though may have profited from off-chain positions. The event highlights that nominal collateral value in DeFi does not equal realizable value during liquidity crises.

marsbit03/16 08:37

Post-Mortem of the Venus THE Attack: How to Profit in a Fleeting Window?

marsbit03/16 08:37

Earning $100,000 in 10 Days: An Interview with OpenClaw's Practical Experience in Prediction Markets

In an interview with Odaily Planet Daily, Kevin, a former ERP architect and Web3 investor, shares how he used OpenClaw to generate a profit of approximately $100,000 in just 10 days, turning a $30,000 investment into over $130,000 at its peak (currently around $112,000). Kevin began his crypto journey during the "inscription summer" of 2023, earning his first significant returns from ORDI. He later transitioned to prediction markets, specifically Polymarket, in mid-2025, attracted by its improved liquidity and user experience. Initially, he used self-developed algorithmic strategies for arbitrage, primarily in sports betting markets, doubling a $100,000 investment over several months. Since integrating OpenClaw in late February, Kevin adopted a hybrid approach: 60% of his strategy remains automated arbitrage, while 40% uses OpenClaw for predictive betting. OpenClaw helps gather and analyze factors like smart money movements, public sentiment, team lineups, and player conditions—even identifying new influencing variables. It also automates backtesting, strategy discovery, and execution, making it effective in Polymarket due to its AI-friendly API. While currently focused on sports markets with limited automated capital ($1,000 per test account), Kevin plans to expand into other domains and may later offer paid OpenClaw "Skills" based on his methodology.

Odaily星球日报03/16 06:25

Earning $100,000 in 10 Days: An Interview with OpenClaw's Practical Experience in Prediction Markets

Odaily星球日报03/16 06:25

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

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

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

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

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