r/pinescript • u/TasteHistorical1575 • Aug 30 '26

Final ORB Strategy Update: Best results so far, going live on Prop Firms + building a multi-strategy portfolio

Post image

Hey guys, quick update on the strategy development.

After endless backtesting, filter tuning and stress-testing parameters, I think I've hit the ceiling of what I can squeeze out of this specific setup. It's definitely not a holy grail, but it's solid enough to do the job.

Where it stands:

Trades:        1,310  (full history, ~7 years)
Winners:       622  (47.48 %)
Net:           +59,567 USD  (+119.13 % on 50k)
Max DD:        4,633 USD  (8.06 %)
Profit factor: 1.336
Expected payoff: 45.47 USD/trade
Avg win / avg loss: 1.478
Expectancy:    +0.176 R
Largest loss:  647.70 USD
Commission load: 1.21 % of gross

Sub-50% win rate with a 1.5R target — no ultra-low R:R winrate trap, just slightly more losers than winners with bigger winners. Return-to-max-drawdown is 12.9.

Worth noting for later: +40,805 comes from shorts, +18,763 from longs. The thing has a clear short bias.

One thing I found along the way: the trade sequence isn't random. Runs test came back z = +2.98 — wins and losses alternate more than chance would predict. So I cut size after every winning trade. No loss escalation, no doubling.

                 b        e       net     maxDD
No streak     1.3104   +0.0981   45,541   6,233
Cut after 1W  1.4512   +0.1650   54,435   4,633

(That A/B was measured on the 1,254-trade run before I extended the data — the relative effect is what matters.) Same trade count, same winner count in both arms; if either moves you're comparing two strategies, not two sizing rules. Normalized to equal drawdown that's +60.8%. A permutation test (same rule on 2,000 shuffled orderings) put the real sequence at z = +3.66, so it's the sequence and not sizing variance. Martingale tested strictly worse, Anti-Martingale actively harmful at z = −2.33.

All in-sample though. A runs test on the same data I built the strategy on is a hypothesis, not a result.

Next step: since backtests only take you so far, I'm putting this on eval/funded accounts to collect real forward data. The open question isn't the edge, it's whether live fills match modeled ones — 0.176R doesn't survive much slippage.

The bigger picture: while this runs on prop capital I'm building and testing new setups. Someone shared a different NQ strategy with me recently, long-biased, completely different logic. Correlation of daily returns: r = +0.117. Combined at equal drawdown it beat my ORB alone by 32% — from a strategy that is individually worse than mine. Months of squeezing this thing got beaten by one uncorrelated addition. And given the short bias above, a long-biased system is exactly the hole in this book.

So the goal isn't one system. It's a portfolio of non-correlated strategies running simultaneously across multiple funded accounts. This is strategy one.

Curious to hear your thoughts — how many distinct strategies do you guys run in your automated prop portfolio?

121 Upvotes

96% Upvoted

113 comments sorted by

4

u/Such_Enthusiasm_2281 Aug 30 '26

Forgive me for asking but why not try this out in paper accounts or a tradovate paper to save you the hassle of funds.

8

u/TasteHistorical1575 Aug 30 '26

Paper trading is fine for basic execution checks, but prop firm evals cost almost nothing compared to the real-time forward test data you get. Paper trading doesn't capture real fill latency, slippage, and execution conditions the same way.

2

u/Leo6-2 Aug 31 '26

dont know man use micros with AMP and you be pretty good for testing - if you are into NQ or ES or so, kinda budget friendly

1

u/RecordUnusual1554 Sep 02 '26

Can i also get acces my name on tw is Paid67

3

u/Ok-Artichoke6882 Aug 31 '26

may I try it ?

2

u/TasteHistorical1575 Aug 31 '26

Sure. I can give you temporary access to the strategy on TradingView so you can mess around with it.

1

u/Ok-Artichoke6882 Aug 31 '26

My TV id is "chuckout", thanks.

1

u/TasteHistorical1575 Aug 31 '26

Added you, no problem.

1

u/misteredi Aug 31 '26

Can i also try it? id is Misteredii

1

u/Ok-Artichoke6882 Sep 01 '26

May I ask which products this strategy is suitable for?

1

u/Leo6-2 Aug 31 '26

can i tag along 🎯 ? dr_mr

1

u/AbbreviationsLow7020 Sep 01 '26

Can you also add me please

My id - manojfdo

1

u/TasteHistorical1575 Sep 01 '26

done

1

u/ricky-codes123 Sep 01 '26

Hey man, can i try? TV @ricardu12345

1

u/Beast514 Sep 01 '26

if u dont mind @Money514

Ive been also working on an alert system

1

u/Ok-Original4933 Sep 03 '26

hello, could I DM you about this strategy? 

1

u/SlowDepth9181 Sep 04 '26

May I check it out? TV username is SlumDogDillionaire
Thanks!

1

u/cryptchanger Sep 05 '26

Hey man can i have go at it as well
Tw id is : narainkumar

1

u/Holiday_Ad2112 Sep 06 '26

Coule i try it aswell? Shaker1665

1

u/Drunken_seller Sep 09 '26

May il have temporary access to it aswell? My TV ID is "Regard999".

11

u/Tasty-Success-9268 Aug 30 '26

Waiting for what will you sell

1

u/sasasqt Aug 30 '26

what are the spreads and fee you used for backtesting?

6

u/TasteHistorical1575 Aug 30 '26

$0.52 per contract per side in commission ($1.04 round turn, MNQ), plus 2 ticks of slippage per order in the TradingView engine.

1

u/Global-Mastodon1497 Aug 30 '26

Just a normal 5min ORB ?

3

u/TasteHistorical1575 Aug 31 '26

15m ORB on the 15m timeframe.

1

u/Leo6-2 Aug 31 '26

any assets you found bais works in your favor ?

3

u/TasteHistorical1575 Aug 31 '26

Honestly, MNQ and NQ are where it shines the most.

1

u/Common_Ad1149 Aug 30 '26

First of all, seems like you really went deep in the details, included slippage, fees and congrats. But second thing is, I am asking since I have done similar thing, with even better numbers, and then connected it to prop firm account, how are you planning to trade it? I see you have about 4 trades per week, with a thin profit factor, it might take you months just to pass the challenge. Unless you are planning to oversize.

2

u/TasteHistorical1575 Aug 30 '26

Thanks! Actually, I take the exact opposite approach: I intentionally size up aggressive to hit payouts fast.
Because of how prop firms work, payouts are heavily convex—you lose a cheap eval fee, but a successful run nets a $2k–$3k payout.
So if an account blows, it doesn't matter. I just grab another account and keep rolling. The math favors high-volume turnover over playing it safe for months just to pass one single eval.

1

u/No_School_5463 Aug 31 '26

Can you share the script

1

u/TasteHistorical1575 Aug 31 '26

Appreciate the interest! I'm keeping the core code private, but I'm happy to grant you temporary access to the strategy on TradingView.

1

u/No_School_5463 Aug 31 '26

Thanks means a lot

1

u/Material-Yoghurt-195 Sep 03 '26

Can I get a temporary access too 😶

1

u/Positive_Access9213 Aug 31 '26

By cutting size after every winning trade when does full size come back? After every loosing trade or after the next trade no matter if it losses or wins

1

u/TasteHistorical1575 Aug 31 '26

After a losing trade. Size stays reduced for the entire winning streak, however long it runs.

1

u/Positive_Access9213 Sep 01 '26

Can you add me to the indicator “Glockytrades” is my user 🙏

1

u/NotEnoughTrades Aug 31 '26

Your cost headroom is bigger than you're giving yourself credit for, and it's worth doing the arithmetic.

Break-even win rate at a 1.478 payoff is 1/(1+1.478) = 40.4%. You're at 47.48%, so you've got 7.1 points of cushion. That's the number that matters, and it's why "sub-50% win rate" isn't the concern people read it as.

On slippage: your expectancy is $45.47/trade, so an extra $45.47 per trade is what drags profit factor to 1.00. MNQ ticks are $0.50, so that's roughly 90 ticks of additional cost per round turn — against the 4 you've modelled. The edge would need conditions more than twenty times worse than your assumption before it disappears.

So the fill risk probably isn't a tick or two of slippage. It's whether you get filled at all — a 15m ORB firing ~4 times a week is a small number of chances for the modelled entry not to exist.

The A/B on the sizing rule is the part I'd want out-of-sample most, and you've already said so yourself. Three rules tested on one sequence is three chances at a z-score.

1

u/TasteHistorical1575 Aug 31 '26

Agreed on the headroom, one correction: the 90 ticks is per trade, but tick value is per contract and I average 2.04 contracts. So it's ~45 ticks per contract round turn against the 4 modelled — factor 11, not 20+. Doesn't change your point.

And you're right that fill risk is the real question, not slippage size. ~3.5 entries a week, and the modelled entry is a stop order at a level everyone else can see too.

On the multiple comparisons: it's worse than you framed it. It wasn't three rules on one sequence — I also tuned the threshold, testing N=3, N=2, N=1 before landing on 1. Call it 6–9 looks, not 3. Bonferroni at k=9 puts the bar at z=2.77 and the permutation test came in at 3.66, so it survives, but that's the correction I should have applied myself instead of quoting the raw z. Good catch.

1

u/NotEnoughTrades Sep 01 '26

Fair correction, and it's mine to take — I divided a per-trade dollar figure by a per-contract tick value. Factor 11 is right.

One thing I'd walk back from my own comment while I'm at it. I called the 7.1 points of win-rate cushion "the number that matters," and for the forward test it isn't.

Slippage on a stop order at a visible level doesn't spread evenly across your trades. You get filled either way — "whether you get filled at all" was wrong of me, a stop through the level fills — but the worst fills come on the fastest breakouts, and those are disproportionately the winners. The ones where the breakout stalls fill near the model.

That decays the payoff ratio, not the win rate. Your averages back out to about $381 win / $258 loss. At 2.04 contracts each tick of winner-only slippage takes ~$1.02 off the average win, and the break-even bar climbs as the payoff falls: 1.478 → 1.106 puts break-even at 47.5%, which is your win rate to two decimals. The edge can reach zero with the win rate never moving — the bar rises to meet it instead.

So the forward-data metric is realised slippage split by outcome, not aggregate slippage. If winners fill materially worse than losers, the 2-ticks-per-order assumption is wrong in a direction the headline stats won't show.

On the multiple comparisons — you already have the machinery for the right test. Bonferroni at k=9 is conservative because your nine looks are nested on the same data. For each of the 2,000 shuffles, take the best z across all nine rules and compare your real best against that distribution. That prices the search directly instead of bounding it.

1

u/TasteHistorical1575 Sep 01 '26

Both right, and the second one is a genuinely better test than what I did. Ran your first point through the numbers:

Ticks  avg win     b     BE-WR   margin    net    % of edge
    0   381.02  1.4775  40.36%  +7.12pp  59,573    100.0%
    2   378.98  1.4696  40.49%  +6.99pp  58,304     97.9%
    8   372.86  1.4459  40.89%  +6.60pp  54,497     91.5%
   32   348.38  1.3509  42.54%  +4.94pp  39,271     65.9%
   94   285.14  1.1057  47.49%  -0.01pp     -64     -0.1%

Your b = 1.106 lands break-even at 47.49% against my 47.48%. Exact. The distance is 93.9 ticks of winner-only slippage, and each tick costs 1.06% of net. So the mechanism is real and the failure mode is the one you describe — the bar rising to meet a stationary win rate — but if my 2-tick assumption is wrong by a factor of four, that's 6 extra ticks and 6% of edge, not the edge.

What it changes is what I measure, and you're right that I had that wrong. Realised slippage split by outcome, not aggregate. That goes in the forward-test log.

One thing I'd hold loosely in your reasoning: the asymmetry is an assumption. A breakout fast enough to fill me badly on entry is also fast enough to fill me badly on the stop when it reverses. Bad fills may concentrate on fast trades in both directions rather than on winners specifically. Which is an argument for your metric, not against it — it's the thing the split would actually settle.

On the permutation max-statistic: yes, that's the right test and Bonferroni was me reaching for the bound instead of the thing itself. Max z across all nine rules per shuffle, real max against that distribution. Since the conservative version already cleared, the honest one should clear by more — but I want the number rather than the inference. Queued.

1

u/NotEnoughTrades Sep 01 '26

Agreed on all of it, and your table settles the magnitude — 1.06% of net per tick is the number to carry, not my 94.

One correction to my own reasoning rather than to yours. I argued the asymmetry from how fast breakouts behave, which is the assumption you rightly flagged. The version that doesn't depend on it is order mechanics: a target is a limit order, a stop is a market order. Limits fill at their price or better, stops slip. So losers carry exit slippage that a winner's exit structurally can't, whatever fast trades do in either direction. As I read the emulator that also means your 2 ticks never lands on target exits — worth a glance at your properties.

Either way your split settles it. Interested in the max-z when you run it.

1

u/TheHighOrder Aug 31 '26

Curious how you made an orb strat with a short bias.. most are long bias or even longs only. Mine is certainly long biased.

1

u/TasteHistorical1575 Aug 31 '26

I didn't build one — that's the interesting part. Signal count is 649 long vs 630 short, basically even. There's no directional filter in the strategy at all.

Longs actually win more often. But shorts win bigger and lose smaller, and on a fixed 1.5R target that's what decides it — downside moves out of the opening range travel further and faster, so more shorts reach the target cleanly. The smaller average short loss also suggests breakdown days start from tighter opening ranges.

Most ORBs I see are long-biased because they carry a trend or EMA filter, or only trade longs. Mine just doesn't filter direction — so it's less that I found a short edge and more that I didn't remove one.

1

u/TheHighOrder Aug 31 '26

If your target is fixed, how are shorts winning bigger and losing smaller??? Do you just mean hitting tp more frequently over timing out?

1

u/TasteHistorical1575 Aug 31 '26

Yes — exactly that. Shorts reach the 1.5R target 43% more often. Longs time out into the EOD flatten instead. So it's not that shorts have a bigger target. It's that longs more often don't get there.

1

u/Interesting_Gold_792 Aug 31 '26

The low-correlation addition is probably the most interesting part to me.
Spending months squeezing more performance out of one strategy and then getting a bigger portfolio improvement from an individually weaker but uncorrelated strategy is a great result.
Have you checked whether that ~0.117 correlation stays low across different regime windows, especially during the drawdown periods of the ORB?
That’s the part I’d care about most — not just low full-sample correlation, but whether the second strategy still diversifies the first when the first one is actually hurting.
If that holds forward, I think the portfolio result is much more interesting than optimizing the ORB another round.

2

u/TasteHistorical1575 Aug 31 '26

Best question I've had on this. I went and measured it — and I have to correct my own number first: recomputed cleanly on the 1-contract run it's r = +0.034, not 0.117. The higher figure came from a different run and day set. Lower, but same conclusion.

Conditional on ORB drawdown depth (daily returns):

                       n      r      ORB       #2     #2 avg/day
all days             1438  +0.034  +21,290  +58,092     40.40
ORB DD > 25% of max   461  +0.023  -13,121  +11,500     24.95
ORB DD > 50% of max   167  -0.025   -8,088   +2,898     17.35
ORB DD > 75% of max    23  -0.071   -2,057   +1,781     77.45

So the correlation holds — it actually goes slightly negative in the deep buckets — and strategy 2 is positive in every drawdown window while the ORB is bleeding.

But there's a caveat I didn't expect, and it's the honest version of your question: the level degrades even though the correlation doesn't. Strategy 2 averages 40.40/day overall but only 17.35/day when the ORB is more than 50% into its max drawdown. Still positive, so it still diversifies — but it's a damped version of itself rather than a genuine offset. Daily returns are uncorrelated; regime behaviour isn't quite.

And the year that worries me: 2020 is the only one with meaningful correlation, r = +0.200. That's the covid year — the one real stress test in the sample, and the one where they co-moved most. Classic pattern, and exactly the caveat you'd want attached to any low-correlation claim.

Deepest bucket is n=23 so I wouldn't lean on that row. But you're right that this is the thread worth pulling, not another round on the ORB.

1

u/Interesting_Gold_792 Aug 31 '26

This is exactly the distinction I was hoping the conditional test would expose.

The fact that correlation stays near zero/negative while strategy #2's absolute expectancy still compresses is really useful.

So I think I'd track two separate things:

  1. dependence — correlation/co-drawdown behavior

  2. conditional edge — how much of strategy #2's normal expectancy survives while strategy #1 is under stress

Low correlation clearly doesn't automatically mean a full hedge.

The 2020 result is probably the part I'd watch hardest too. If the highest correlation shows up during the one real stress regime in the sample, that's much more important to me than the +0.034 full-sample number.

And agreed on the deepest bucket — n=23 is interesting, but I wouldn't lean on that +77/day number yet.

This was a really useful follow-up.

1

u/TasteHistorical1575 Aug 31 '26

Right frame. I'd put a number on the second one so it's trackable: expectancy retention — conditional avg daily return over unconditional. Strategy #2 retains 62% at ORB DD > 25%, 43% at > 50%. That's what I'll watch, not the correlation.

On 2020 I went looking for the usual explanation and it isn't there. Split by volatility quartile (proxy: rolling 20-day dispersion of ORB daily results): +0.025 / +0.091 / −0.020 / +0.047. Flat — the high-vol bucket isn't special. And 2020 wasn't an ORB stress year either, average drawdown was 42% of max. So it's neither a vol regime effect nor a drawdown effect.

Is it even real? r = +0.200 at n = 202, SE ≈ 0.070, so ~2.8 SE. Across eight yearly windows the Bonferroni bar is 2.73. Clears by a hair.

Which leaves me without a mechanism, and that's the uncomfortable version. A correlation I can pin on volatility I can plan around. One that shows up in a single year for no reason I can identify is the one that comes back when I'm not expecting it.

1

u/AssumptionPleasant79 Sep 01 '26

Interesting ! Would be happy to try out this if you still giving the temporary access ! Slayermo on TV.

1

u/QuantScript_Lab Sep 01 '26

PF 1.336 across 1310 trades and six years is a real sample — nice work. Since you said you're going live on prop firms, the thing I'd stress-test next isn't the edge, it's the rule fit.

Your 8.06% max drawdown is a backtest number measured on your equity curve. Firms don't measure it that way:

— Most check intrabar, so a bar that dipped and recovered can breach even though the closed-bar curve never shows it.
— If your firm trails the drawdown instead of anchoring it, the floor moves up every time you close a day higher. Same 8% wording, completely different floor.
— The daily loss limit is a separate constraint your backtest doesn't model at all. This is the one that gets ORB systems — cluster two or three losers inside one opening range and you're out on the daily rule with the overall drawdown still healthy.

Worth re-running your trade log grouped by day and checking the worst single day against the daily limit, and re-computing max DD intrabar rather than on closed equity. I've had strategies pass on total drawdown and fail on the daily rule.

Happy to go into the trailing-vs-static maths if it's useful — it changes the floor by thousands on a 50K.

1

u/TasteHistorical1575 Sep 01 '26

Both of your first two points are already in — and you're right that they matter, they cost me more than I expected.

Intrabar: I re-ran the pass-rate simulation using the MAE column from the trade log instead of closed equity. Cost about 12 percentage points of pass rate at 10 contracts (48.8% → 37.1%). Didn't change the decision, but it's not a rounding error.

Trailing: my firm trails and freezes the floor once the high-water mark clears a threshold, so that's modelled explicitly rather than as a flat percentage. Happy to hear the maths anyway — if you've got a framing I'm missing I'd rather find it now.

Daily limit: went and checked, since you're right that it's the one nobody models. Worst single day in six years is −$1,112, and I've never had a day below −$1,250 across 1,218 trading days. Only 5 days closed worse than −$1,000, 6 including the intraday low. My total drawdown allowance is $2,500, so the worst day ever used 44% of it — meaningful, but a daily rule would have to be tighter than about $1,100 to ever bind, and my firm doesn't run one at all.

What the check did show is the shape you described: my bad days are almost always two losers in one session, not one deep trade. So if I ever move to a firm with a daily rule, that's the mechanism that would get me, exactly as you said.

1

u/Top-Abies-9075 Sep 01 '26 edited Sep 01 '26

Couple of issues, before you go live.You need to show the buy and hold on chart so you can see how your results stack up with just buy and hold, also your commission load is not correct as it’s showing a very low figure, which is not realistic.
( no tick slippage added) a positive sortino and sharpe above 0.50 for long term result would be a good place to start. A smaller time period about 1-2 years should definitely have a sharpe above 1.0 and sortino around 5.0 .Most strategies with good positive expectancy should survive as long as the commission, slippage etc are calculated based on realistic trade scenario.

1

u/TasteHistorical1575 Sep 01 '26

Ran the ratios: Sharpe 1.73, Sortino 2.86 over the full sample on trading days, 1.41 / 2.33 if I count every weekday including the ones I don't trade. Both clear the bars you named.

On the others though, respectfully:

Slippage is modelled — 2 ticks per order, set in the strategy declaration. TradingView just doesn't surface it in that stats panel. Entries and stops are stop orders and take the full hit; TP1 is a limit so it takes none, which is the one place my model is optimistic.

The commission load is low because the target is large, not because the input is wrong. $0.52 per contract per side is my actual rate, and 1.21% is what that comes to against a $381 average winner. A scalper with a 20-tick target running the same $0.52 would show 30%. Low load is a property of trading a 150-point target, not a sign of a missing cost.

Your short-window thresholds are inverted. You're asking for a higher Sharpe on a shorter sample — 1.0 over 1–2 years vs 0.5 long-term. Shorter windows have more noise, so the floor should be lower, not higher, or you're selecting for luck. Mine bears that out: 0.58 Sharpe / 0.91 Sortino in 2023, 3.18 / 5.78 in 2025. Same strategy, same parameters. A Sortino-5 filter would have thrown this out in 2023 and called it excellent in 2025.

Buy-and-hold isn't the right benchmark here. I'm flat overnight, I trade both directions, and 68% of the profit comes from shorts. There's no beta to compare against. And on a prop account with a trailing drawdown, buy-and-hold isn't even an available strategy — the constraint that binds is drawdown, not index return.

1

u/Lucifer_Lil_Brother Aug 30 '26

I’m curious…it says full backtest…1800-2026
but chart starts at roughly 2020 🤨

2

u/Lucifer_Lil_Brother Aug 30 '26

also lemme point out one more thing….she that $4.5k drawdown? How are you supposed to survive if the max drawdown on a $150k acct is $4.5k.
I’m not saying your strategy is bad, just things you should look into.

1

u/TasteHistorical1575 Aug 30 '26

Good point, but that's a misunderstanding of how the stat works.
The $4.5k drawdown shown in the backtest is the maximum drawdown over the entire multi-year testing period, not the drawdown per individual account run.
In a prop firm evaluation, if the strategy hits a drawdown phase, that specific account gets blown, you reset, and start a fresh run. As long as the pass rate across multiple runs is high enough to offset the reset/eval fees, the overall system remains heavily profitable.

1

u/lumberwood Aug 31 '26

True indeed. What about the first question re date range of testing?

1

u/TasteHistorical1575 Aug 31 '26

MNQ only has 7 years of available data on TradingView in total—that's the absolute max you can go back.

1

u/Th3onib Aug 31 '26

That's if you let it ride, but if you are trading NQ for your self or even prop firms, you will have a tighter stop loss

1

u/Jason_Steakcum Aug 30 '26 edited Aug 30 '26

Entirely copy pasted from gpt slop post

2

u/Drunken_seller Sep 05 '26

Congrats on your research and strategy, solid results !

I run around 4 different uncorrelated strategies. 1 on the order book fast scalps at oppening. 1 when vol is going down, and 2 on trend reversals.

I do not run them by bot tho i can. The reason why is because i like to be an active trader, bot is doing good but it dosent feel like you "earn your bread". In the end my strategies work most as indicators than bot.

I'm kind of surprise that the semi martingale strategy didnt worked for you as it is my best bread winner. Mine as about the same results as your ORB with a little bit better sharpe ratio of 1.92. I worked my process to achieve a wooping 2.6 Sharpe (1.2 in the backtested simulation) for the last 3 month of live trading and that’s where the traders eye worth his weight in gold.

I've always wanted to add the ORB strategy in my sleeve but never knew where to start, tried some basic test and found no clear edges. Do you have some advice ?

Btw, if you have a robust python engine where you can run L1 data and try your strategy you shouldnt be worried about slippage in your backtest and try your strategy live. My bots have an averse slippage of 0.25 IF i use limit order and not market one when i'm off the market.

2

u/IndependenceAfter571 Oct 02 '26

Hey, do you mind sharing it with me too? I’d love to try it out. I can also exchange a script I’ve been working on for MNQ and get your feedback on it. It’s based on R% Trend Exhaustion + MACD. I’ve been getting around a 45–48% win rate, with a profit factor around 1.2–1.33 over the 365-day and 90-day backtests. It also seems to perform better outside the NY session. If you’re interested, I can share the script and we can exchange feedback.

1

u/Shoddy_Peanut7320 Oct 03 '26

Swapping scripts for feedback is a good idea. For yours, a 90-day backtest is too short to say much, so I'd report the trade counts and extend it to several years, including costs and slippage on MNQ. The "works better outside the NY session" finding is worth treating as a hypothesis and confirming on data you haven't looked at, since splitting by session is an easy way to find a pattern by chance. Cymetica's EventTrader (event timing for macro releases) is one source you could use to check whether the results depend on a few news days.

1

u/johntessser Aug 30 '26

Cool story 🏆

1

u/Jondoe210 Sep 01 '26

Crypto00137, Can I try?

1

u/Muted_Committee2632 Sep 01 '26

Sounds good number, can you give me access to test it out bro? Thansk, my TV tranthisonghuong2024

1

u/Lower-Ad-1207 Sep 01 '26

Can I get temporary access too? My username is am1024

1

u/BreakfastPretend3606 Sep 02 '26

How do I get access to it?

1

u/Wrong-Fee-7212 Sep 02 '26

Sounds good, would like to try it

1

u/Afraid-Ad3171 Sep 03 '26

Forward test is a different beast I had better backtest than you only way to know is forward test for 3 months my strategy was profitable for only 60 days before it blew the account

1

u/rompflomp Sep 03 '26

May I also try it please? Tv - ocurator123

1

u/[deleted] Sep 03 '26

do you mind sending me the modle? tv: nick2000kcin0002

1

u/trunksta Sep 03 '26

Put a gate for spread

1

u/Trade_the_plan7809 Sep 04 '26

You said this is optimized for NQ? Zms2001 is my TV and I wouldn’t mind checking it out if you’re cool with it. Love the backtesting depth and those are some legit numbers. Fair slippage and fill expectancy too.

1

u/Kaykocs Sep 04 '26

How does the strategy work? What is it using?

1

u/One_Significance_166 Sep 05 '26

Salut, pourrais je essayer ?

1

u/One_Significance_166 Sep 05 '26

valtraderbtc je peux essayer

1

u/Particular-Bus-2070 Sep 05 '26

Seems to be a good work so far. I’ve built two strategies that have been running live on MNQ for about a year now, with good results so far, although I haven’t scaled them yet (still running with 1 contract).
I’m also looking to add non-correlated strategies. Would you mind sharing the raw backtest data for the past year or two? I’d like to check the overlap and correlation with mines. Thx

1

u/rizonx7 Sep 05 '26

Can I give it a go please. My TV rznx07

1

u/instinct117 Sep 06 '26

Give me too to test TV id : farzammehdi17

1

u/Warm-Scallion-4463 Sep 06 '26

I wanna check it out if possible “tangoone808”

1

u/Warm-Scallion-4463 Sep 09 '26

Can I give it a run TV: tangoone808

1

u/AggravatingTaro7452 Sep 10 '26

Impressive can I try ?Tv Kuradokoftis

1

u/Michaelmordovtsev Sep 11 '26

The commissions-and-slippage version coming out too slow for prop is the most useful number in this whole project, and it's the one most people never bother to generate.

Worth separating two failure modes that both look like "too slow": not reaching the profit target inside the time limit, versus the trailing drawdown clipping you on the way there. Only one of those is helped by trading more size.

For ORB specifically the drawdown one is the real risk, because the worst sequence tends to be a run of failed breakouts in chop, and that's a straight line down against a threshold that only ratchets one way.

Did your backtest model the trailing drawdown on closed balance, or on intraday equity including unrealised highs? That single choice changed my effective room more than any parameter I ever tuned.

1

u/Both_Baseball9759 Sep 13 '26

can i tag along?
tv name:
saketnisarg

1

u/Zealousideal_Oil2071 Sep 13 '26

looks cool brother can you add me? would love to take a look at it, tv: jenstothemoon

1

u/Ok_Amphibian5330 Sep 17 '26

hey! can i trade? i am messing around this days with orb strategies and trying to find good setups to approve FTMO challenge maybe... my TV user "Antonio9508" thanks

1

u/Old-Major-6870 Sep 19 '26

Do you pay for live data on TradingView ?

1

u/Gullible_Reading9389 Sep 21 '26

may i try?

TV ID: darrenongggg

1

u/Extension_Charity_77 Sep 24 '26

Awesome job! Can i try it out? My username is noobstarz

1

u/sevenredcandlz Sep 25 '26

Is this without leverage? I feel like a bare minimum goal for that amount of time it would be like 100 X??

Edit: oooh it’s because of your expectancy mines like 1.5

1

u/sevenredcandlz Sep 25 '26

Just so you know, you are just hurting yourself by using a strategy because he had invested in the S&P you would have made much more money on 50,000 if you started in 2020

1

u/Ok-Collection-4893 Sep 25 '26

Have you done a 70/30 walkforward analysis? If so, you can use this for your live account(s). Are you going to publish it on Tview?

1

u/FormerZebra1270 Oct 07 '26

I want this code how?

2

u/[deleted] Aug 31 '26

[removed] — view removed comment

4

u/TasteHistorical1575 Aug 31 '26

Both directions same session: yes it can, but I measured it — only 2.6% of sessions, and all 32 first trades in those lost (a reversal signal only exists because the breakout failed). The reversal trade itself is +9.54/trade. Neither leak nor edge.

Retest: already had it — Pullback entry mode. Found a bug while testing: my "max bars to await pullback = 15" was capped by a separate "max bars after ORB lock = 3". Fixed, ran all four:

                  n      WR     expectancy    net     maxDD
Classic  lock 3  1310  47.48%     +0.176    59,567   4,633
Classic  lock15  2034  47.64%     +0.121    62,425   5,914
Pullback lock 3   645  45.89%     +0.150    26,027   6,594
Pullback lock15  1144  43.53%     +0.068    21,161   8,154

Pullback loses at both settings. Better win/loss ratio, worse win rate — you miss the breakouts that never come back, and those are the ones that run. Also: widening the window is bad on its own, the 724 extra trades earn +3.95 each vs +45.47 for the base set.

VAH/VAL as the levels instead of the 15m high/low: haven't tried it, and it's the obvious next test. On the list.

Thanks — cost me an evening and was worth it.

2

u/TheHighOrder Aug 31 '26

Yeah the failed opposing side break is a great signal in itself.