The operation of shorting altcoins uses AI to develop automated monitoring robot programs for abnormal signals.

Recently, opportunities for short squeezes in altcoins have frequently appeared. The author has successfully captured the $VOXEL market through an AI-built monitoring system. This article will deeply analyze the strategy logic and the method of building the monitoring robot, along with a complete implementation plan.

Market Background Analysis#

The current cryptocurrency market shows risk-averse characteristics influenced by macro policies, with liquidity in risk assets continuously shrinking. Holders of a large number of altcoins face a dual dilemma:

Exhausted liquidity in the spot market makes it difficult to sell
Traditional market-making channels see soaring costs

The perpetual contract market has become a new battlefield. By cleverly designing short squeeze strategies, market makers can realize liquidity cashing through retail short positions. This new game theory creates arbitrage opportunities for sharp traders.

I. Comprehensive Analysis of Short Squeeze Strategies#

Five-Step Method for Market Makers#

Liquidity Dilemma
Holding a large amount of spot while facing the embarrassment of a sell-off leading to a crash
Contract Breakthrough
Creating an exit channel through forced buying when short positions are stopped out/liquidated in perpetual contracts
Short Induction Strategy
Raising spot prices (affecting the mark price) to induce retail investors to short
Rate Arbitrage
Accumulation of short positions leads to negative rates, allowing market makers to earn double profits by holding long positions
Ultimate Harvest
Pushing up to key resistance levels to trigger short liquidations, completing the distribution of chips

The essence of this strategy is to transform shorts into liquidity providers through the contract market, achieving chip transfer under zero-sum games.

II. Core Monitoring Indicator System#

The complete short squeeze cycle presents the following data characteristics:

Extreme negative funding rate → Abnormal growth in OI → Breakthrough resistance level → Long/Short ratio decreases → OI retreats

Key monitoring across four major dimensions:

1. Funding Rate Anomalies#

Threshold: <-0.1% (significant signal of market maker control)
Monitoring Points: Continuous expansion of negative values for 3 cycles

2. Open Interest Fluctuations (OI)#

Trigger Conditions: 3-cycle average/10-cycle average > 2
Data Source: Binance perpetual contract API

3. Price Breakthrough Behavior#

Verification of key resistance level breakthroughs
Liquidation heatmap to assist judgment

4. Long/Short Ratio Regression#

Long/Short Ratio ≤ 0.8 warning
Monitoring of large holder position changes

III. AI Monitoring System Construction Guide#

Build an automated monitoring system using Python + Telegram Bot, with core implementation logic:

Data Collection Module#

# Key parameters for Binance API
endpoint = "https://fapi.binance.com/fapi/v1/premiumIndex"
params = {
    'symbol': 'BTCUSDT',
    'interval': '5m',
    'limit': 100
}

Need to collect 10-dimensional data:

Mark price/index price
Basis and percentage
Funding rate/OI
Long/Short account ratio
Large holder position ratio
Taker buy/sell ratio

Data Processing Logic#

Frequency: Every 5 minutes
Storage: CSV archived by coin type
Path: data/{symbol}_futures.csv

Intelligent Warning System#

Composite trigger conditions:

(abs(funding_rate) > 0.001) 
& (MA(OI,3)/MA(OI,10) > 2) 
& (price > resistance_level)

When conditions are met, trigger Telegram Bot push, including:

Coin name
Current funding rate
OI growth multiple
Key resistance level information

Operation Suggestions#

Prioritize altcoins with a market cap of $100 million to $300 million
Validate market maker address movements with on-chain data
Set dynamic take-profit and stop-loss at 5%-8%
Avoid high leverage operations (recommended ≤ 3x)

Original @AI Soros Kote

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