Stochastic Momentum Strategy

Pine ScriptMomentum Mean reversion Trend following RSI EMA SMA Stochastic

Author: ChaoZhang
Complexity: Intermediate · 85 lines
Parameters: 16
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

The Stochastic Momentum Strategy is a quantitative trading strategy that combines the Stochastic Momentum Index (SMI) and the Relative Strength Index (RSI). It uses the SMI to identify overbought and oversold areas in the market, with the fast RSI acting as a signal filter. It also implements a body filter for more reliable signal selection.

Strategy Principles

Stochastic Momentum Index

The Stochastic Momentum Index (SMI) is a common technical indicator used in quantitative trading that combines the strengths of momentum and oscillation indicators.

Specifically, the SMI is calculated as:

SMI = (Close - (HH + LL)/2)/(0.5*(HH - LL)) * 100

where HH is the highest price over the past N days, and LL is the lowest price.

So the SMI incorporates both the trend-following judgment of momentum and the reversal judgment of oscillation. Values above 80 are considered overbought, while values below 20 are oversold. The strategy generates trading signals when SMI reaches these overbought or oversold levels.

Fast RSI

The Relative Strength Index (RSI) is a standard overbought/oversold indicator. This strategy uses a fast RSI with a period of 7 to judge short-term overbought/oversold conditions.

Readings below 20 are considered oversold, while those above 80 are deemed overbought per the fast RSI. Signals are generated when these thresholds are breached.

Body Filter

The strategy also implements a body filter by checking the candlestick body size to filter certain signals. Only bodies exceeding a set threshold will trigger trades.

This filters out some false signals and increases reliability.

Advantages

Multi-Indicator Combo

This approach combines the SMI, fast RSI, and body filter into a robust 3-part system. Using multiple integrated signals improves accuracy and enhances stability.

Overbought/Oversold Detection

Both SMI and fast RSI are excellent for detecting exhausted trends. By trading mean-reversions from these overextended areas, the strategy adheres to buying low and selling high.

Two-Way Trading

The ability to both buy dips and short rallies maximizes opportunities across market conditions.

Risk Control

The body filter avoids whipsaws by rejecting low-conviction signals in choppy conditions.

Risks

Whipsaws

Frequent long/short switching brings whipsaw risk. Optimizing logic could minimize this.

Crowded Trades

Signals may cluster market participants and spur quick reversals upon entry. Fine-tuning parameters could reduce herding risk.

Black Swans

Extreme events can upend all models. Intelligent stop losses are necessary to control systematic risks.

Enhancements

Parameter Optimization

Testing different SMI/RSI periods and body filter thresholds could uncover optimal values for higher returns.

Dynamic Stops

Incorporating volatility-based or ATR stops would better contain position and portfolio risk.

Machine Learning

Models predicting future indicator levels could identify turning points earlier. This would enhance predictive power.

Conclusion

In summary, by integrating the SMI, fast RSI, and body filter, this strategy has created a fairly comprehensive overbought/oversold system. The multi-signal approach improves accuracy, while two-way trade capability and risk controls contribute to balance. With continual parameter and model optimization, it shows promise for capturing gains over the long run.

Parameters

Name	Default	What it controls
v_input_1	true	Long
v_input_2	true	Short
v_input_3	false	Use Martingale
v_input_4	100	Capital, %
v_input_5	true	Use SMI Strategy
v_input_6	true	Use RSI Strategy
v_input_7	true	Use Body-Filter
v_input_8	5	SMI Percent K Length
v_input_9	3	SMI Percent D Length
v_input_10	50	SMI Limit
v_input_11	2017	From Year
v_input_12	2100	To Year
v_input_13	true	From Month
v_input_14	12	To Month
v_input_15	true	From day
v_input_16	31	To day

Source Pine Script

/*backtest
start: 2023-12-22 00:00:00
end: 2024-01-21 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//Noro
//2018

//@version=2
strategy(title = "Noro's Stochastic Strategy v1.1", shorttitle = "Stochastic str 1.1", overlay = false, default_qty_type = strategy.percent_of_equity, default_qty_value = 100, pyramiding = 0)

//Settings 
needlong = input(true, defval = true, title = "Long")
needshort = input(true, defval = true, title = "Short")
usemar = input(false, defval = false, title = "Use Martingale")
capital = input(100, defval = 100, minval = 1, maxval = 10000, title = "Capital, %")
usesmi = input(true, defval = true, title = "Use SMI Strategy")
usersi = input(true, defval = true, title = "Use RSI Strategy")
usebod = input(true, defval = true, title = "Use Body-Filter")
a = input(5, "SMI Percent K Length")
b = input(3, "SMI Percent D Length")
limit = input(50, defval = 50, minval = 1, maxval = 100, title = "SMI Limit")
fromyear = input(2017, defval = 2017, minval = 1900, maxval = 2100, title = "From Year")
toyear = input(2100, defval = 2100, minval = 1900, maxval = 2100, title = "To Year")
frommonth = input(01, defval = 01, minval = 01, maxval = 12, title = "From Month")
tomonth = input(12, defval = 12, minval = 01, maxval = 12, title = "To Month")
fromday = input(01, defval = 01, minval = 01, maxval = 31, title = "From day")
today = input(31, defval = 31, minval = 01, maxval = 31, title = "To day")

//Fast RSI
fastup = rma(max(change(close), 0), 7)
fastdown = rma(-min(change(close), 0), 7)
fastrsi = fastdown == 0 ? 100 : fastup == 0 ? 0 : 100 - (100 / (1 + fastup / fastdown))

//Stochastic Momentum Index
ll = lowest (low, a)
hh = highest (high, a)
diff = hh - ll
rdiff = close - (hh+ll)/2
avgrel = ema(ema(rdiff,b),b)
avgdiff = ema(ema(diff,b),b)
SMI = avgdiff != 0 ? (avgrel/(avgdiff/2)*100) : 0
SMIsignal = ema(SMI,b)

//Lines
plot(SMI, color = blue, linewidth = 3, title = "Stochastic Momentum Index")
plot(SMIsignal, color = red, linewidth = 3, title = "SMI Signal Line")
plot(limit, color = black, title = "Over Bought")
plot(-1 * limit, color = black, title = "Over Sold")
plot(0, color = blue, title = "Zero Line")

//Body Filter
nbody = abs(close - open)
abody = sma(nbody, 10)
body = nbody > abody / 3 or usebod == false

//Signals
up1 = SMIsignal < -1 * limit and close < open and body and usesmi
dn1 = SMIsignal > limit and close > open and body and usesmi
up2 = fastrsi < 20 and close < open and body and usersi
dn2 = fastrsi > 80 and close > open and body and usersi
exit = ((strategy.position_size > 0 and close > open) or (strategy.position_size < 0 and close < open)) and body

//Trading
profit = exit ? ((strategy.position_size > 0 and close > strategy.position_avg_price) or (strategy.position_size < 0 and close < strategy.position_avg_price)) ? 1 : -1 : profit[1]
mult = usemar ? exit ? profit == -1 ? mult[1] * 2 : 1 : mult[1] : 1
lot = strategy.position_size == 0 ? strategy.equity / close * capital / 100 * mult : lot[1]

if up1 or up2
    if strategy.position_size < 0
        strategy.close_all()
        
    strategy.entry("long", strategy.long, needlong == false ? 0 : lot, when=(time > timestamp(fromyear, frommonth, fromday, 00, 00) and time < timestamp(toyear, tomonth, today, 23, 59)))

if dn1 or dn2
    if strategy.position_size > 0
        strategy.close_all()
        
    strategy.entry("Short", strategy.short, needshort == false ? 0 : lot, when=(time > timestamp(fromyear, frommonth, fromday, 00, 00) and time < timestamp(toyear, tomonth, today, 23, 59)))
    
if time > timestamp(toyear, tomonth, today, 23, 59) or exit
    strategy.close_all()

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-22. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

Build this with AgenKit

A spec is not a trading system. Paste this into your coding agent and AgenKit runs a specialist team — architecture, test-first build, risk gates, review — with paper trading on by default.

in Claude Code, Cursor, Codex & 4 more

/agenkit build a production-grade trading bot with a backtest engine, risk controls and paper trading by default from strategies/Stochastic-Momentum-Strategy.md

npx agenkit install engineering-kit in your project
Paste the command into your agent and approve each phase
Get tests, backtest, risk controls and a paper-trading switch

Get AgenKit How /agenkit works

Understand it first

Before you run this, read the lesson behind the idea and how to backtest it honestly. Specs like this one are the survivors of an unknown number of trials; deflate their results accordingly.