VWAP RSI Oversold Crossunder BTC Short Strategy

This is a BTC short strategy across timeframes based on the RSI indicator of VWAP. It calculates the Volume Weighted Average Price (VWAP) of each candlestick to get a VWAP curve, and then applies the RSI indicator to…

Pine ScriptMean reversion Trend following Volatility RSI MACD VWAP

Author: ChaoZhang
Complexity: Intermediate · 76 lines
Parameters: 12
Backtest bar: 1m · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

Calculate VWAP of each candlestick to get a VWAP curve
Apply RSI indicator to VWAP curve, with length of 20 days, overbought level at 85 and oversold level at 30
When RSI crosses down from overbought zone (85) to oversold zone (30), open short position
Close position after holding for 28 candlesticks, or if RSI crosses oversold line (30) again

Advantage Analysis

Use VWAP instead of simple close price to reflect actual trading price
Identify overbought/oversold status with RSI to avoid buying high and selling low
Trade across timeframes to avoid being trapped
Controllable risk with 28 candlesticks stop loss

Risks & Solutions

Price surges rapidly due to black swan events, unable to stop loss
- Adopt across timeframes trading to reduce risks of being trapped
Inappropriate parameter settings, easily miss opportunities
- Test and optimize RSI parameters and overbought/oversold levels
RSI unable to cross into oversold zone
- Combine with other indicators to determine trend, adjust parameters flexibly

Optimization Directions

Test more parameter combinations to find optimum
Combine with MACD, KD etc to judge overbought/oversold status
Test parameter settings separately for different assets
Optimize stop loss mechanism, set stop loss range based on volatility

Summary

This strategy identifies BTC overbought/oversold status with the combination of VWAP and RSI. By trading across timeframes, it can effectively control risks. The strategy logic is clear and easy to understand, worth further testing and optimizing for live trading.

Parameters

Name	Default	What it controls
v_input_1		Resolution
v_input_2	20	RSI Length
v_input_3	30	RSI Oversold level
v_input_4	85	RSI Overbought level
v_input_5	28	Number of candles
v_input_6	true	Enter longs ?
v_input_7	true	Enter shorts ?
v_input_8	2020	Strategy Start Year
v_input_9	true	Strategy Start Month
v_input_10	true	Strategy Start Day
v_input_11	false	Use Laguerre on RSI ?
v_input_12	0.06	Laguerre Gamma

Source Pine Script

/*backtest
start: 2023-12-21 00:00:00
end: 2023-12-28 00:00:00
period: 1m
basePeriod: 1m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/


//@version=4

strategy("Soran Strategy 2 - SHORT SIGNALS", pyramiding=1, initial_capital=1000, default_qty_type=strategy.percent_of_equity, default_qty_value=50, overlay=false)


// ----------------- Inputs ----------------- //

reso = input(title="Resolution", type=input.resolution, defval="")
length = input(20, title="RSI Length", type=input.integer)
ovrsld = input(30, "RSI Oversold level", type=input.float)
ovrbgt = input(85, "RSI Overbought level", type=input.float)
lateleave = input(28, "Number of candles", type=input.integer)
// lateleave : numbers of bars in overbought/oversold zones where the position is closed. The position is closed when this number is reached or when the zone is left (the first condition).

// best parameters BTCUSDTPERP M15 : 20 / 30 / 85 / 28


stratbull = input(title="Enter longs ?", type = input.bool, defval=true)
stratbear = input(title="Enter shorts ?", type = input.bool, defval=true)

stratyear = input(2020, title = "Strategy Start Year")
stratmonth = input(1, title = "Strategy Start Month")
stratday = input(1, title = "Strategy Start Day")
stratstart = timestamp(stratyear,stratmonth,stratday,0,0)


// --------------- Laguerre ----------------- //

laguerre = input(title="Use Laguerre on RSI ?", type=input.bool, defval=false)
gamma = input(0.06, title="Laguerre Gamma")

laguerre_cal(s,g) =>
    l0 = 0.0
    l1 = 0.0
    l2 = 0.0
    l3 = 0.0
    l0 := (1 - g)*s+g*nz(l0[1])
    l1 := -g*l0+nz(l0[1])+g*nz(l1[1])
    l2 := -g*l1+nz(l1[1])+g*nz(l2[1])
    l3 := -g*l2+nz(l2[1])+g*nz(l3[1])
    (l0 + 2*l1 + 2*l2 + l3)/6


// ---------------- Rsi VWAP ---------------- //

rsiV = security(syminfo.tickerid, reso, rsi(vwap(close), length))

rsiVWAP = laguerre ? laguerre_cal(rsiV,gamma) : rsiV


// ------------------ Plots ----------------- //

prsi = plot(rsiVWAP, color = rsiVWAP>ovrbgt ? color.red : rsiVWAP<ovrsld ? color.green : color.white, title="RSI on VWAP", linewidth=1, style=plot.style_line)
hline = plot(ovrbgt, color = color.gray, style=plot.style_line)
lline = plot(ovrsld, color = color.gray, style=plot.style_line)
fill(prsi,hline, color = rsiVWAP > ovrbgt ? color.red : na, transp = 30)
fill(prsi,lline, color = rsiVWAP < ovrsld ? color.green : na, transp = 30)


// ---------------- Positions: only shows the Short and close shoret positions --------------- //

timebull = stratbull and time > stratstart
timebear = stratbear and time > stratstart


strategy.entry("Short", false, when = timebear and crossunder(rsiVWAP, ovrbgt), comment="")
strategy.close("Short", when = timebear and crossunder(rsiVWAP, ovrsld)[lateleave] or crossover(rsiVWAP, ovrsld), comment="")

Published by ChaoZhang on the FMZ strategy square · last modified 2023-12-29. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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