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Strategy vault / Mean reversion

Pivot Support Reversal Indicator Strategy

This strategy combines the Pivot Support Reversal indicator with support/resistance levels to track trends and manage profit/drawdown. The rules are: 1. Go long when the PSR indicator generates a buy signal 2. Take…

Author
ChaoZhang
Complexity
Starter · 26 lines
Parameters
0
Backtest bar
3d · position
Tested on
BTC/USDT · spot

Strategy Logic

This strategy combines the Pivot Support Reversal indicator with support/resistance levels to track trends and manage profit/drawdown.

The rules are:

  1. Go long when the PSR indicator generates a buy signal

  2. Take 25% partial profit at R1

  3. Take another 25% partial profit at R2

  4. Use a moving stop loss below the 14-period moving average minus 3xATR

The PSR indicator synthesizes CMO, Bollinger Bands, volume and more into high-probability signals. Pivot points act as profit targets while having trend-following ability. The strategy's strength lies in its staged profit taking and disciplined stop loss to lock in profits while tightly controlling risk.

Advantages

  • PSR combines multiple factors for high-quality signals

  • Pivots act as profit targets and tracking tools

  • Staged profit taking and trailing stop protects profits and manages risk

Risks

  • PSR parameters need optimization

  • Pivots can sometimes be breached

  • Risk remains for residual position after partial profits

Summary

This strategy capitalizes on the PSR indicator's syndicated signals and uses pivots as dynamic profit targets. By taking profits in batches and cutting losses fast, it aims to pragmatically book profits while tightly controlling risk.

Source Pine Script

/*backtest
start: 2023-01-01 00:00:00
end: 2023-09-13 00:00:00
period: 3d
basePeriod: 1d
exchanges: [{"eid":"Binance","currency":"BTC_USDT"}]
*/

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © ParaBellum68

//@version=4
strategy(title="SOJA PIVOT", shorttitle="SOJA PIVOT")
soja = ((cmo(close,5) > 25) and (cmo(close,5) < 70) and (close> close[1]) and (bbw(close,50,1) < 0.6) and (sum(volume,5)> 250000) and (obv[5]>15))
TP = 2.1 * hlc3[1]- high[1]
TP2 = TP + high[1] - low[1]
SL = avg(close,14) - 3*atr(14)
strategy.entry("buy", true, 1, when = soja == 1)
strategy.close("buy", when = close > TP)
strategy.close("buy", when = close > TP2)
strategy.exit("stop", "exit", when = close < SL)




Published by ChaoZhang on the FMZ strategy square · last modified 2023-09-14. 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/Pivot-Support-Reversal-Indicator-Strategy.md
  1. npx agenkit install engineering-kit in your project
  2. Paste the command into your agent and approve each phase
  3. Get tests, backtest, risk controls and a paper-trading switch

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.

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