Smart Money Index (SMI) Strategy Backtest

Cryptocurrency

Created: 2023-09-21 21:14:02

Last modified: 3 years ago

Copy: 0

ChaoZhang

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Overview

This is a quantitative trading strategy based on the Smart Money Index (SMI). The index reflects activities of institutional funds and is used to forecast potential market trends by analyzing SMI movements. It belongs to trading strategies based on investor sentiment analysis.

Strategy Logic

The core indicator is the Smart Money Index (SMI). Its calculation formula is:

SMI = SMA(Today's close - Today's open + Yesterday's close - Yesterday's open, N)

Where N is the parameter period.

The SMI reflects inflows and outflows of smart money. Rising SMI indicates net inflows, meaning smart money is bullish. Falling SMI indicates net outflows, meaning smart money is bearish.

The trading strategy goes long when SMI rises and goes short when SMI falls, to follow the movements of smart money.

Advantages

Based on SMI, captures smart money activities
Simple SMI calculation, easy to implement
Reflects investor sentiment, sensitive to market changes
Applicable across products and timeframes
Parameter tunable for adaptability

Risks

SMI itself may lag
Prone to whipsaws relying on single indicator
Cannot differentiate bull/bear markets, needs TA
No effective stops, large drawdowns
Parameters need optimization by product and timeframe

Risks can be reduced by:

Optimizing SMI parameter period
Adding technical indicators for confirmation
Implementing stop loss/profit rules for risk control
Parameter tuning based on product and timeframe
Adjusting position sizing system

Enhancement Directions

The strategy can be improved by:

Finding optimal SMI calculation period
Adding filters like MACD on SMI signals
Incorporating moving or fixed stops
Product-specific parameter optimization
Identifying ideal periods for different timeframes like hedge funds
Adjusting position sizes by market volatility

Summary

This strategy uses the Smart Money Index to reflect market participant sentiment for trend trading. It can capture moves of institutional funds in a timely manner. However, SMI may lag and sole reliance on one indicator can be risky. Improvements can be made through parameter tuning, adding filters, implementing stops, and dynamic position sizing. This can make the strategy more robust.

Source

Pine

/*backtest
start: 2022-09-14 00:00:00
end: 2023-09-20 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=2
////////////////////////////////////////////////////////////
//  Copyright by HPotter v1.0 01/08/2018
// Attention:

Strategy parameters