When Both Thursdays And Fridays Are Down In SPY (S&P 500 Reversal Trading Strategy)
Last Updated on August 6, 2022
Mondays and Fridays often turn out to be reversals when they move hard either way.
Today, we look at a reversal strategy that happens on Fridays. The results are very good with an average gain of 0.77% per trade.
(This article was originally published in June 2013. We updated the backtest on our Substack service.)
A reversal trading strategy in the S&P 500:
- Today is Friday.
- Yesterday (Thursday) was down more than -0.15% and today also more than -0.15%. 0.15% is used to have some wiggle room because you have to send the orders before the market closes.
- Exit at the close after two days (usually on Tuesdays).
The test period is from 2005 until February 2013:
| P/L | #Fills | Avg. | |
| 47.93 | 62.00 | 0.77 |
Here is the equity curve:
Doing the opposite (and short) we get this equity curve:
It does not work on other days. On Wednesdays, it pays off to go short.
——————————
If you would like to have the Amibroker and Tradestation code for this strategy plus 70+ other free trading strategies published on this website, please click on this link:
For more trading strategies, please click here:
- Free trading strategies
- Monthly trading edges (subscription service)
Â
Hi,
Here’s a small script that replicates your strategy in R (http://www.r-project.org):
install.packages(c(“quantmod”, “lubridate”))
library(quantmod)
library(lubridate)
posSize <- 5000 # per trade
txnFees <- 0.02 # per share
# Get data from Yahoo Finance
x <- getSymbols("SPY", from = "2005-01-01", auto.assign = FALSE)
# Adjust OHLC and subset data from 2005-01-01 to 2013-02-28
x <- adjustOHLC(x["2005::2013-02"], use.Adjusted = TRUE)
# Signal: if Friday and last two close to close were down
x$Wday <- wday(index(x))
x$Sig <- ifelse(x$Wday == 6 & Cl(x) < Lag(Cl(x)) & Lag(Cl(x)) < Lag(Cl(x), 2), 1, 0)
# Entry price: close price of sig day
x$EntryPrice <- as.numeric(Cl(x))
# Exit price: close price 2 days later
x$ExitPrice <- c(as.numeric(Cl(x))[-c(1:2)], rep(NA, 2))
# Number of shares
x$Shares <- floor(posSize/x$EntryPrice)
# Calculate PnL including txn fees
x$PnL <- x$Sig * x$Shares * (x$ExitPrice – x$EntryPrice – 2 * txnFees)
x[is.na(x)] <- 0
plot(cumsum(x$PnL/posSize))