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 successfully captured the $VOXEL market through an AI-built monitoring system. This article will deeply analyze the strategy logic and the method for 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 large amounts of altcoins face a double dilemma:

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

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

I. Full Analysis of Short Squeeze Strategy#

Five-Step Method for Market Makers#

Liquidity Dilemma
Holding a large amount of spot assets but facing the predicament of a sell-off leading to a crash
Contract Breakthrough
Creating an exit channel through forced buying during short stop-loss/liquidation in perpetual contracts
Short Induction Strategy
Raising spot prices (affecting the mark price) to induce retail investors to short
Rate Arbitrage
Accumulation of shorts 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 convert shorts into liquidity providers through the contract market, achieving chip transfer under zero-sum game conditions.

II. Core Monitoring Indicator System#

The complete short squeeze cycle presents the following data characteristics:

Extreme negative funding rate → OI abnormal growth → Breakthrough resistance level → Long/short ratio decline → OI retreat

Key monitoring across four dimensions:

1. Funding Rate Anomalies#

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

2. Open Interest Sudden Changes (OI)#

Trigger Condition: 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 Setup Guide#

Building 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
}

Data needs to be collected across 10 dimensions:

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 Alert 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 notifications, 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 stop-loss and take-profit at 5%-8%
Avoid high leverage operations (recommended ≤3x)

Original @AI Soros Kot

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