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.

The Advancing MM 1: Market Maker Inventory Quoting System

The article "Advancing MM 1: Market Maker Inventory Quoting System" explains why retail traders often experience price movements against their positions after buying altcoins. It argues this is not due to market manipulation but is a result of automated market maker (MM) systems responding to inventory risk and toxic order flow. When traders place large buy orders, MMs sell inventory, creating a short exposure. To mitigate this risk, MMs adjust their pricing using the Avellaneda-Stoikov model. They skew quotes lower to attract sellers and widen spreads to reduce transaction probability, aiming to return inventory to a balanced state while protecting against adverse selection. The reservation price, central to this model, is calculated as the mid-price adjusted by inventory levels and risk aversion. The article notes that retail traders are particularly affected because their orders are often large, concentrated, and not hedged—especially in low-liquidity altcoins. A suggested strategy for traders is to break large orders into smaller, less detectable increments, allowing them to buy at progressively better prices as the MM’s quoting system reacts. The post teasers follow-up articles on order book dynamics and toxic flow.

marsbit12/28 04:18

The Advancing MM 1: Market Maker Inventory Quoting System

marsbit12/28 04:18

Advancing MM 1: Market Maker Inventory Quoting System

"Attack of the MM 1: Market Maker Inventory Quoting System" by Dave explores why altcoin prices often move against retail traders immediately after their purchases, debunking the myth of intentional manipulation by "market manipulators." The article explains that this phenomenon is not due to malicious intent but is a result of automated market maker (MM) systems using the Avellaneda-Stoikov model for inventory-based pricing and protection against toxic order flow. When retail traders execute large buy orders, MMs sell, leading to a short inventory exposure. To mitigate risk, MMs adjust their strategies in two ways: 1. **Quote Skew**: They lower prices to attract sellers and discourage further buys, aiming to replenish inventory and protect their short position. 2. **Spread Widening**: They widen bid-ask spreads to reduce transaction probability and earn more spread profit to offset potential losses. The core mechanism involves the "Reservation Price," calculated as Mid Price − γ⋅q (where q is inventory and γ is risk aversion). Large retail orders disrupt inventory balance, causing MMs to adjust prices dynamically. Retail traders often face this due to their concentrated, unconcealed, and unhedged orders, especially in low-liquidity altcoins where their trades significantly impact pricing. The article concludes with a practical tip: instead of executing large orders at once, retail traders can break them into smaller, staggered orders to exploit MM pricing adjustments, achieving better average entry prices. A follow-up will discuss toxic order flow and order book dynamics.

深潮12/28 04:12

Advancing MM 1: Market Maker Inventory Quoting System

深潮12/28 04:12

The Advancing MM 3: Statistical Edge and Signal Design

Title: The Attack of MM 3: Statistical Edge and Signal Design Author: Dave This article, the third in the "Attack of MM" series, explores how market makers (MMs) actively gain a "micro alpha" advantage through statistical edges and signal design, rather than just passively adjusting quotes. Micro alpha refers to a conditional probability shift in predicting short-term price movements (within ~100ms to ~10s), such as the direction of the next price change, mid-price drift, or trade asymmetry. It is not about forecasting trends but detecting probabilistic biases that allow MMs to act preemptively—buying before likely price increases, withdrawing bids before declines, or reducing exposure during risky periods. Key signals discussed include: - **Order Book Imbalance (OBI)**: Measures the normalized volume difference between buy and sell orders near current prices. - **Order Flow Imbalance (OFI)**: Tracks aggressive taker orders that drive price changes. - **Queue Dynamics**: Analyzes order queue behavior, including hidden orders (icebergs) and spoofing (fake large orders to manipulate perception). - **Cancel Ratio (CR)**: Indicates liquidity withdrawal rates, signaling market instability. The article emphasizes that speed is MMs' absolute advantage. Lower latency enables faster reaction to market events, facilitating latency arbitrage by executing orders before competitors. In crypto exchanges, some players even get priority execution rights, highlighting the importance of speed and access. Finally, the author notes the complexity of real-world MM strategies and hints at future topics like dynamic hedging and options.

深潮12/28 04:11

The Advancing MM 3: Statistical Edge and Signal Design

深潮12/28 04:11

Attack on MM 2: Market Maker Order Book and Order Flow

"Attack of the MM 2: Market Maker Order Books and Order Flow" by Dave explores the dynamics of market making in crypto, focusing on the Limit Order Book (LOB) and order flow. The LOB is the market maker's battlefield, defined by depth, tick size, and the concept of price improvement—providing better prices than the current best bid/ask to create market value. Market makers profit from the bid-ask spread, which covers order processing costs, inventory risk (holding assets amid price fluctuations), and adverse selection risk (being exploited by informed "toxic" traders). The spread types include quoted, effective, and realized spread, with the latter factoring in future mid-prices to measure true profitability. Order flow involves managing incoming trades, with a focus on identifying "toxic flow" from informed traders who can cause losses. Market makers use metrics like VPIN (Volume-weighted Probability of Informed Trading) to detect one-sided pressure and respond by pulling quotes, widening spreads, or reducing order sizes to mitigate risk. The article hints at deeper institutional strategies in future installments.

深潮12/28 04:03

Attack on MM 2: Market Maker Order Book and Order Flow

深潮12/28 04:03

29 Million Impressions, Only $71 Earned: How to Make Money from Traffic on X Platform?

An experiment on X platform revealed that chasing high exposure metrics does not translate to significant earnings. After generating 28.9 million impressions and gaining 37.5k followers through aggressive commenting and engagement chasing under popular posts, the author earned only $71.49. The key insight is that exposure alone is a vanity metric. X’s monetization algorithm prioritizes quality interactions from paid verified users—not raw visibility. Comments and reposts from premium users carry the most weight, while likes and free-user engagements have little value. The author’s high-exposure strategy attracted bot traffic, diluted audience quality, and risked algorithmic demotion. The account was flagged as low-quality despite high impression numbers. The revised strategy focuses on nurturing a genuine community: removing fake followers, creating original content, and fostering meaningful interactions with verified users. Key metrics for success now include a 3-5% engagement rate, over 45% verified user ratio, and comment-driven interactions. The conclusion: X rewards content creators who build authentic communities, not those who exploit exposure loopholes.

深潮12/26 06:25

29 Million Impressions, Only $71 Earned: How to Make Money from Traffic on X Platform?

深潮12/26 06:25

The Truth of Trading: A Numbers Game of Patterns and Probabilities

The Truth of Trading: A Numbers Game of Patterns and Probability Most traders fail not due to a lack of methods or information, but because they misunderstand the nature of trading. Mark Douglas, in "Trading in the Zone," redefines the market as a probabilistic environment where an edge only materializes over a sufficiently long period. Trading is not about prediction or seeking certainty; it is a numbers game of pattern recognition. A valid trading pattern does not guarantee that any single trade will be profitable. It merely indicates a historical probability of success. Each individual trade outcome is random, but the overall probability distribution over many trades is not. Traders must evaluate performance like a casino: focus on long-term expectation and repeated execution, not single wins or losses. Accepting that "anything can happen" is liberating. It removes the emotional sting from losses, enables disciplined stop-loss execution, and eliminates hesitation. The ideal "flow state" is not excitement but emotional neutrality—executing the plan without attachment to outcomes or need to be right. Ultimately, traders cannot control results, but they can control their execution. Success comes from emotional detachment and consistent repetition. When traders stop trying to prove themselves right and let the probabilities work over time, they align with the true nature of the market: a numbers game based on pattern recognition and disciplined repetition.

深潮12/26 02:45

The Truth of Trading: A Numbers Game of Patterns and Probabilities

深潮12/26 02:45

The Truth of Trading: A Numbers Game of Patterns and Probabilities

The Truth of Trading: A Numbers Game of Patterns and Probabilities Most traders fail not due to a lack of methods or information, but because they misunderstand the nature of trading. Mark Douglas, in "Trading in the Zone," redefines trading: it is not about prediction or certainty, but a probabilistic environment where edges manifest only over time. Thus, experienced traders summarize it as a pattern-recognition numbers game. Trading isn’t forecasting; it’s executing a plan amid uncertainty. No single trade can be guaranteed. Patterns don’t predict outcomes—they only define probabilistic edges. A valid pattern means historically higher chance of profit, not a promised win. Losses don’t invalidate the method; they are part of randomness. Individual trade outcomes are random, but the overall probability distribution isn’t. Profit comes from expectancy multiplied by repetition, not single trade accuracy. Accepting "anything can happen" liberates traders: losses feel less offensive, stop-losses are executed cleanly, and emotional interference fades. The "flow state" is emotional neutrality—no need to prove correctness or fear mistakes. It’s loyalty to the process. Trading is a numbers game: identify edges, repeat executions, and let large samples reveal results. Many traders intellectually agree but emotionally reject this: they judge themselves per trade, expect every pattern to work, take losses personally, and abandon strategies after few failures. The key isn’t a better method, but correct execution. You can’t control outcomes, but you can control execution. Patterns offer probability, not promises. Consistency requires emotional detachment and repetitive discipline. When traders stop proving themselves right and let probabilities work, trading succeeds.

marsbit12/26 01:59

The Truth of Trading: A Numbers Game of Patterns and Probabilities

marsbit12/26 01:59

On-Chain Metrics Practical Guide: Identifying Real Signals, Avoiding Data Traps

A practical guide to on-chain metrics for traders, focusing on identifying genuine signals and avoiding data traps. Key concepts include distinguishing between transaction fees (user-paid costs), protocol revenue (actual earnings), and MEV (maximal extractable value), emphasizing that sustainable revenue matters more than high fees. Total Value Locked (TVL) is often misleading due to double-counting, incentive-driven "mercenary capital," and idle stablecoins. Traders should analyze TVL alongside transaction volume and incentives. Daily Active Addresses (DAA) can be inflated by bots and airdrop farmers; it’s only meaningful when correlated with fees and real activity. Cross-chain bridges enable asset transfers but carry risks like smart contract vulnerabilities and centralization. Monitor bridge volumes for liquidity flow insights. Stablecoin supply acts as crypto’s money supply (M2); increasing supply suggests market liquidity, while decreases may signal withdrawals. Token unlocks and emissions create sell pressure; avoid tokens nearing large unlocks unless trading short-term. The ratio of transaction volume to TVL indicates capital efficiency—high ratios reflect active usage, while low ratios suggest "ghost liquidity." In summary, on-chain metrics are analytical tools, not absolute truths. Cross-verify signals and interpret data contextually for informed decisions.

比推12/25 13:06

On-Chain Metrics Practical Guide: Identifying Real Signals, Avoiding Data Traps

比推12/25 13:06

Bitcoin and Ethereum: Two Paths, Dead Cat Bounce or Bottom Breakout? Altcoins: Highs and Lows for SOL, LIQUID, PIPPIN, AAVE

Bitcoin and Ethereum are at a critical juncture, with BTC attempting to break above the $88,800 resistance amid significant selling pressure. A failure to hold above this level could lead to a pullback toward supports near $87,720–$85,220. Ethereum follows a similar pattern, needing to reclaim $3,010 to confirm a short-term uptrend; otherwise, it may test lower supports around $2,970–$2,906. The broader market remains cautious, with potential U.S. market corrections likely affecting crypto. Altcoins present mixed opportunities. SOL faces resistance near $131, with key support at $120–$121.2. AAVE, despite internal issues, is considered a quality DeFi project—dips may offer buying opportunities. PIPPIN hit its $0.26 target, demonstrating disciplined trend trading. LIQUID introduces an innovative auto-LP mechanism, while GUA, an AI-based project, shows signs of accumulation near $0.10. Conversely, ASTER is viewed as overvalued with weak fundamentals—any bounce may be short-lived. Traders are advised to avoid heavy positions, focus on swing trading during volatility, and wait for clear breakouts before committing. Risk management is essential in the current uncertain environment.

金色财经12/23 07:20

Bitcoin and Ethereum: Two Paths, Dead Cat Bounce or Bottom Breakout? Altcoins: Highs and Lows for SOL, LIQUID, PIPPIN, AAVE

金色财经12/23 07:20

December 23 Market Analysis: Why the Decline Today? BTC, ETH, BNB, SOL, LUMIA, PORTAL, ASR, ME Altcoin Trading Suggestions

Cryptocurrency markets declined 1.25% over the past 24 hours amid a broader risk-off sentiment and year-end liquidity constraints. Key factors include the shutdown of approximately 400,000 Bitcoin miners, leading to $87 million in liquidations and selling pressure, a 121% surge in derivatives trading volume amplifying the downtrend, and fear-driven capital rotation into Bitcoin, whose dominance rose to 58.98%. Liquidations totaled $263 million, with 85,980 traders affected—$185 million from long positions and $77.79 million from shorts. Major cryptocurrencies like BTC ($87,313, -1.82%), ETH ($2,957, -2.33%), BNB ($848, -1.27%), and SOL ($124, -1.30%) faced pressure, with critical resistance levels identified at $88,800, $3,010, $861, and $126 respectively. Failure to hold these levels may lead to further declines toward key support zones. Market sentiment remains fearful (index at 25), with macroeconomic uncertainty and delayed Fed rate cut expectations until 2026 driving volatility. Top gainers included LUMIA (+36%) and PORTAL (+17%), while ASR (-16%) and ME (-12%) led losses.

金色财经12/23 07:16

December 23 Market Analysis: Why the Decline Today? BTC, ETH, BNB, SOL, LUMIA, PORTAL, ASR, ME Altcoin Trading Suggestions

金色财经12/23 07:16

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