(function(){
var CN = 'menthorq_utm_params';
var LK = 'menthorq_utm_params';
var UK = ['utm_source','utm_medium','utm_campaign','utm_term','utm_content','utm_id'];
var CK = ['gclid','fbclid','msclkid','ttclid','twclid'];
var CD = 30;
var AK = UK.concat(CK);function sC(n,v,d){var e=new Date(Date.now()+d*864e5).toUTCString();var c=n+'='+encodeURIComponent(v)+';expires='+e+';path=/;SameSite=Lax';if(location.protocol==='https:')c+=';Secure';document.cookie=c;}
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function ld(){var r=gC(CN);if(r){try{var n=nm(JSON.parse(r));if(n)return n;}catch(e){}}try{var s=localStorage.getItem(LK);if(s){var n=nm(JSON.parse(s));if(n)return n;}}catch(e){}return null;}
function mg(p,n){var o={};if(p)for(var k in p)o[k]=p[k];for(var k in n)o[k]=n[k];return o;}var ps = new URLSearchParams(window.location.search);
var fd = {}, has = false;
for (var i = 0; i < AK.length; i++) {
var v = ps.get(AK[i]);
if (v) { fd[AK[i]] = v; has = true; }
}// Click-ID synthesis: when only a click-id is present (no utm_source), derive
// utm_source/utm_medium so downstream analytics groups under the right channel.
var SY = {
gclid: ['google', 'cpc'],
fbclid: ['facebook', 'cpc'],
msclkid: ['bing', 'cpc'],
ttclid: ['tiktok', 'cpc'],
twclid: ['twitter', 'cpc']
};
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for (var sk in SY) {
if (fd[sk]) { fd.utm_source = SY[sk][0]; fd.utm_medium = SY[sk][1]; break; }
}
}if (has) {
fd.captured_at = new Date().toISOString();
var ex = ld();
// Last-touch: merge new fields ON TOP of previous last (preserva campi pregressi)
var newLast = ex && ex.last ? mg(ex.last, fd) : fd;
// First-touch: se ex.first ha almeno un UTM, e' completo e sticky.
// Se ex.first esiste ma e' click-id-only (orphan), completa con i campi nuovi.
// Se ex.first non esiste, usa fd come first.
var newFirst;
if (ex && ex.first) {
var firstHasUtm = false;
for (var i = 0; i < UK.length; i++) if (ex.first[UK[i]]) { firstHasUtm = true; break; }
newFirst = firstHasUtm ? ex.first : mg(ex.first, fd);
} else {
newFirst = fd;
}
sv({first: newFirst, last: newLast});
return;
}var raw = gC(CN);
if (raw) {
try {
var p = JSON.parse(raw);
if (!p.first && hk(p)) sv({first: p, last: p});
} catch(e) {}
return;
}try {
var s = localStorage.getItem(LK);
if (s) { var n = nm(JSON.parse(s)); if (n) sv(n); }
} catch(e) {}
})();
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//# sourceURL=breeze-prefetch-js-extra
In this Guide we want to share the Swing Model Backtesting February 2025 Results during the beginning of the earning season in February 2025. We are looking at the week of 02/03/2025 to 02/07/2025. Companies like Google, Palantir, Amazon, Uber and more reported that week.
We go over the full backtest during our Live here below:
Backtesting Assumptions
The backtest has the following assumptions:
Data was taken from MenthorQ Swing Trading Model as of Sunday 02/02/2025. The levels from Sunday are calculated after market close on the previous Friday 01/31/2025.
We then took the Bias given by the model weather Bullish or Bearish and we downloaded the various levels: Upper Band, Lower Band and Risk Trigger.
We then trade at the Open of Monday 02/03/2025.
The entry price is at the open and the exit price uses the next Friday close 02/07/2025.
We then create different baskets and for all those baskets we apply two strategies:
Strategy 1: Long / Short Stock
Long / Short Underlying Stock. We trade the Stock at the open of Monday 02/03/2025.
Entry: Going Long if the Swing Bias was Bullish and Going Short if the Bias was Bearish.
Exit: We close the trade at the close of Friday 02/07/2025
Strategy 2: Selling Credit Spreads
The second type of strategy leverages the Swing Trading Levels and Bias to define our Credit Spreads. These are the assumptions:
If Bias is Bearish we sell a Call Credit Spread using the Upper Band as the level for our Sold Call
If Bias is Bullish we sell a Put Credit Spread using the Lower Bans as the Level for our Sold Put
We define our success rate shows if at the close of Friday the options expire worthless (Out of the Money)
Here you can find the File with the Data and Results.
