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 exhibits risk-averse characteristics influenced by macro policies, with liquidity in risk assets continuously shrinking. Holders of large amounts of altcoins face a dual dilemma:

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

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-Inducing Strategy
Raising spot prices (affecting the mark price) to induce retail investors to short.
Rate Arbitrage
Accumulation of shorts leads to negative funding 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 a zero-sum game.

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 dimensions:

1. Funding Rate Anomalies#

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

2. Open Interest Fluctuations (OI)#

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

3. Price Breakthrough Behavior#

Verification of breakthrough at key resistance levels
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, 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 collection 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 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 Kot

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