Like a lot of people here I went down the ICT/smart-money rabbit hole — sweeps, CISDs, FVGs, breakers, the lot. The problem was never finding concepts. The problem was that nobody could tell me which ones actually hold up, because almost nobody tests them. So I did, with a simple rule: if I couldn't write it as code, I didn't get to believe in it.

Scale of the testing, so you know this isn't three screenshots and a feeling: 830k+ liquidity-sweep confirmations, 390k+ reversal events, 1.6M+ continuation events, across 14 markets (crypto, indices, FX, metals) and 5+ years of data, fees included.

Some of what died:

• Waiting for the retest. Winners retest shallow or not at all; losers retest deep. On 8,000+ confirmations, the pullback everyone waits for was usually the market telling you the trade already failed.
• "Smart" trade management. Breakeven at +1R, trailing behind structure, cutting on a close through the level — every variant gave edge back versus a fixed target on the same trades. All of them.
• First-touch POI magic, most stop-buffer rules, sweep-count rules. Felt great on hindsight charts. Zero edge in the data.

Some of what survived:

• The sweep-and-reclaim itself. C1 range → raid → close back inside → close through the level that delivered the raid. Real and measurable.
• Higher-timeframe context. The same trigger that's noise on its own timeframe becomes signal inside a daily/weekly parent. Shows up in the numbers every time.
• Speed of confirmation. Winning reversals get their lower-timeframe confirmation within the hour. Slow confirmation is a dead trade. Surprised me the most.
• The continuation shift inside fresh context. Standalone it loses over 1.6M events. Inside a just-confirmed reversal it was the strongest entry we measured.

Full disclosure: I turned this into a TradingView indicator (the Distilled Model) because I got tired of drawing it manually, and yes, it's a paid tool — that's the marketing part of this post and I won't pretend otherwise. It draws only what survived: the sweeps, confirmation levels, stage labels so you know if you're early or late, reversal/continuation shifts, and a liquidity map for targets. Nothing repaints.

But honestly, even if you never touch the indicator, take the two free findings: stop waiting for deep retests, and stop managing your winners into breakeven scratches. Those two came out of the data over and over, on every market we tested.

Happy to post the backtest breakdowns for any specific claim in the comments — genuinely enjoy being challenged on this stuff.

https://www.tradingview.com/script/i8UN0L3B-The-Distilled-C2-Indicator/

https://whop.com/the-distilled-indicator/the-distilled-indicator-the-only-levels-you-need-on-your-charts/

Code & result ChatGPT (pinescript API) share
https://chatgpt.com/share/6a4b6909-feb8-83ee-99e4-b2ca8f9eed71

I backtested an optimized BTC 3-hour EMA regime strategy, then ran a walk-forward test to see if the result held up out-of-sample.

Strategy summary:

- Market: BTCUSD
- Timeframe: 3H
- Direction: long only
- Regime filter: EMA10 > EMA60 > EMA200, price above EMA200, EMA200 rising
- Entry: EMA cross or pullback reclaim of EMA10
- RSI filter: RSI(14) >= 55
- Stop: 2.5x ATR(14)
- Exit: close below EMA85 or EMA10 < EMA60
- Position size: 95% equity
- Commission: 0.1%
- No pyramiding

In-sample result, 2019-01-01 to 2026-07-06:

Metric	Result
Net PnL	+2,803.26%
Final equity	$100k to $2.90M
Max drawdown	26.65%
Profit factor	2.23
Trades	123
Win rate	43.09%

This looked strong, but it was clearly optimized in-sample, so I do not treat the +2,803% as a realistic expectation.

Walk-forward test:

Each fold optimized on a rolling 2-year window, then tested on the next unseen period.

OOS Period	Return	Max DD	PF
2021	+19.56%	21.32%	1.33
2022	-16.34%	16.34%	0.24
2023	+28.32%	13.29%	1.86
2024	+57.94%	9.35%	3.13
2025	+17.55%	6.41%	2.11
2026 YTD	-5.68%	8.27%	0.51

My conclusion:

The strategy is partially robust, but not fully robust. It works well during BTC uptrend regimes, but struggles in bear markets or choppy downtrends. The 2022 and 2026 YTD folds are the obvious weak spots.

The main improvement needed is better bear-market defense, maybe a higher-timeframe trend filter, volatility regime filter, or drawdown-based cash mode.

Curious what you would test next. Would you use a weekly EMA filter, volatility filter, cash mode, or something else? you can test more on chatgpt link with 5.5 model

Works for me. Basically my 2026 market cipher clone. I also have a tweaked version that I run of MC (2nd pic)

I made this strategy with GPT, based on the Q-Trend indicator.

It can backtest, give feedback on the results, and improve the strategy from there. I think this GPT can do way better when it can backtest, because it can actually understand how its Pine Script performs. I think you guys should try this, since I think it’s free right now. I use the GPT-5.5 model, and the paid version works way better with Thinking Mode.

leave link i used GPT : https://chatgpt.com/g/g-69e705ab3e6081919ce0c92e1f567e5d-pinescript-api

Spent the last few weeks tweaking the entry logic on the ICT indicator. The old version was triggering too late – price would already be moving away from the zone by the time I got the signal.

New version detects wick rejections inside the candle. It's basically the same strategy but with much better timing.

TP and SL levels also fixed. Signals are cleaner, stops are tighter, increasing the strategies R:R.

If you're one of the people testing it, you'll see the update. Let me know what you think.

I developed a custom trading signal that I now use for my own trading. Previously, I spent a significant amount of time performing manual market analysis. To streamline the process, I coded my strategy in Pine Script, which has drastically reduced the time required before entering a trade.
The signal is based on a momentum trading strategy that combines RSI, ADX, EMAs, and multi-timeframe analysis. With the analysis now automated, my workflow is much simpler: I monitor high-impact news events to avoid getting caught in volatile market conditions, wait for a valid trading signal, and execute the trade.

https://www.tradingview.com/script/lrqCHzke-Stryk-Ranges/

Strategy

Docs

I have been working on a small TradingView strategy optimiser, built by Jayadev Rana, to make Pine Script testing less slow and repetitive.

The idea is simple:

Instead of manually changing inputs again and again, the tool tests many random combinations of strategy parameters and shows which ones are worth reviewing.

It is not meant to find some magic profitable setting. I know optimisation can easily overfit if you trust the result blindly.

For me, the useful part is speed.

It helps with:

- testing different parameter ranges

- finding interesting combinations faster

- comparing results without doing every setting manually

- spotting which ideas deserve deeper testing

- rejecting weak setups earlier

The real work still comes after that: out-of-sample checks, realistic costs, walk-forward testing, and proper risk control.

I built this because I was tired of guessing settings by hand inside TradingView. This workflow makes the research process cleaner and faster.

Curious what other Pine Script users think:

Would you prefer this kind of optimiser inside TradingView, or do you usually export the logic/data and optimise outside TradingView in Python?

Been building out my own execution bot for a while now and finally feel like it's in a solid place worth talking about.

The setup: TradingView sends webhook alerts to a FastAPI server I built, which then places real orders directly on my TopstepX funded account via their API. No manual intervention — signal fires, order goes in, stops and targets hit the broker automatically.

Running two strategies through it:

Hull Regime Bot runs overnight on MNQ on a 7m chart. It's a trend-following setup — when the Hull MA flips direction it enters a flip trade on the new side and starts a bot-side trailing stop that steps up every $50 in unrealized P&L. The idea is to let the overnight trend run and lock in gains as it moves. No fixed stop, no target — just ride the move until Hull says otherwise or the trail catches it.

The Strat Bot runs RTH. This one looks for specific Strat bar patterns (3-1-2s, 2-1-2 reversals, 2-2 reversals) and Failed 3-2 setups on 5m. Fixed 3R target on the Strat side, real SL and TP orders sitting on the broker the moment the entry fills.

Goal is to pass multiple TopstepX evaluations, get funded, and run both strategies across accounts simultaneously.

Still validating live but the bot infrastructure is locked in. Took a while to get right — more edge cases than I expected when you're dealing with a real broker API.

So, I was discussing with claude about EA and algo trading. After a very long discussion, i made my mind that for a noob like me, building an EA is an easy approach.
After sometime, i was watching random videos on yt about trading strategies. I was watching strategy video of a Pakistani forex trader, and casually decided that i will build an EA based on this strategy.
The strategy was uploaded in 2 parts on YouTube. So here are the steps the I follow:
Copy the links and transcripts of both videos
Paste in claude
Asked the claude to build an .mq5 file
Claude give me first one with errors and 1 warning. Then it removed all errors and warnings on my request, it gave me a clear file.
That file i run on mt5 and back test it the strategy. I gave me horrible results. Here i am attaching results.
I also asked the claude to describe results to me, here is the response of:
“Let me pull every number from your three screenshots together first, then walk through what each term means and what the overall picture tells you.
The Headline Result
You started with $5,000 and ended $2,403.58 down — roughly a 48% loss of your test capital, leaving about $2,596. Along the way, your account also suffered an 85.81% drawdown, meaning at its worst point it lost over four-fifths of its peak value. Reading the numbers together, the account actually grew quite a bit first (a 7-trade winning streak added about $9,697, likely pushing the balance up toward roughly $13,900) before a brutal 9-trade losing streak and other losses gave almost all of it back and then some.
Direct answer to your question: as currently configured, this is not a strategy you’d want to run on a real account. Not because the core pattern idea is necessarily worthless, but because the risk management around it let losses snowball into a near-account-destroying swing. I’ll explain why below, and what to change.
Every Term, Explained
Term
Your Result
What It Means
Gross Profit
$33,304.45
The sum of every single winning trade added together (ignoring losses)
Gross Loss
-$35,708.03
The sum of every single losing trade added together
Total Net Profit
-$2,403.58
Gross Profit + Gross Loss. Your actual real result.
Profit Factor
0.93
Gross Profit ÷ Gross Loss. Above 1.0 = profitable overall. Below 1.0 (like yours) = you lost more than you made.
Drawdown (Balance)
85.81% ($11,956.52)
The biggest drop from a peak account value to the lowest point after it, before any recovery. This is the single most important risk number — yours is dangerously high. Most professional risk limits cap this at 10–20%.
Drawdown (Equity)
87.11% ($12,739.13)
Same idea, but also counting the floating (unrealized) loss of trades that were still open at the worst moment, not just closed ones.
Expected Payoff
-$19.38
The average profit or loss per single trade. Negative means, on average, every trade you take loses about $19.
Sharpe Ratio
-1.60
Measures return relative to risk/volatility taken. Positive and above 1.0 is good; negative (like yours) means the risk taken wasn’t rewarded — you took on volatility and got punished for it.
Recovery Factor
-0.19
Net Profit ÷ Max Drawdown. Tells you how well profits “make up for” the worst drawdown. Negative simply reflects that net profit itself is negative.
AHPR
1.0034 (+0.34%)
Average Holding Period Return — the simple average % return per trade. Slightly positive on its own.
GHPR
0.9947 (-0.53%)
Geometric Holding Period Return — the compounded average return per trade, accounting for the fact that each trade’s size depends on the account balance at the time. This is the more “honest” number.
AHPR vs GHPR gap
—
This is actually a useful diagnostic: when GHPR is meaningfully lower than AHPR (like here), it’s a mathematical signature of high volatility/swings in trade outcomes — exactly matching the huge drawdown you saw.
Z-Score
0.28 (22.05%)
A statistical check for whether your wins/losses are randomly distributed or “streaky” beyond what chance would predict. Close to 0 means your sequence of wins/losses looked close to random — no unusual hidden pattern.
LR Correlation
-0.53
How closely your equity curve matches a smooth straight line going up, measured from -1 to +1. A great strategy is close to +1 (steady climb). Yours is negative, meaning the overall trend line actually slopes downward with a lot of noise around it.
LR Standard Error
$2,402.75
How far, on average, your actual equity curve wandered away from that straight-line trend — in dollars. Nearly half your starting deposit, which confirms a very choppy, non-smooth equity curve.
Margin Level
89.41%
How much of your account’s required margin was being used at the riskiest point. Below 100% is margin-call territory for most brokers; 89.41% means this test got dangerously close to a forced stop-out.
A couple more numbers worth knowing: you took 124 trades (248 “deals,” since every trade has an opening deal and a closing deal). Win rate was 43.55% overall — sells (56 trades) won 46.43% of the time, buys (68 trades) won only 41.18%. Average win was $616.75 against an average loss of $510.11, a roughly 1.2:1 reward-to-risk ratio — reasonable, but not enough to overcome a sub-50% win rate. The one number that stands out as a red flag on its own: your largest single loss was $3,814.97 — about 7.5 times your average loss, which suggests one trade either had a much bigger position size than the others or wasn’t properly contained by its stop loss.
Why It Likely Went This Wrong
Two things in the EA’s current default settings are the most probable culprits, and both are easy to fix:
Position sizing compounds with balance. The EA risks a fixed percentage of your current balance per trade. During that 7-trade win streak, your balance grew substantially — which means every trade after that streak was sized larger in dollar terms than your earlier trades. When the 9-trade loss streak hit right after, it was hitting on inflated position sizes, which is exactly the mechanism that turns a normal losing streak into an 85% drawdown. If InpRiskPct was left above 1%, this effect gets even more severe.
The trend filter was off by default. In the original videos, Ahmad Umair was explicit: trade with the main H1/H4 trend for full targets, and only take counter-trend setups for a quick 1:1. The EA’s InpTrend input defaults to false, meaning it was taking every valid pattern regardless of the higher-timeframe trend, treating with-trend and counter-trend setups identically. Your lower win rate on longs versus shorts is consistent with this — if price was generally choppy or declining through much of this test window, untrend-filtered buy signals would underperform.
What I’d Change Before Re-Testing
In order of impact: lower InpRiskPct to 0.5–1% if it isn’t already there, turn InpTrend on to true so it only takes patterns aligned with the bigger trend, and turn InpHighProbOnly on to true so it only takes the 80–90% same-color-doji setups rather than the lower-probability mixed-color ones. After that, re-run the same backtest and compare — I’d expect the drawdown number especially to drop significantly. It’s also worth extending your date range; ~5–6 months of data (which is what 11,181 M15 bars works out to) is a reasonably solid trade sample (124 trades) but a fairly short window to know if this is representative of how gold behaves across different conditions.
If you check your Inputs tab and tell me what InpRiskPct and InpTrend were actually set to in this run, I can tell you with much more confidence exactly how much of this drawdown came from sizing versus the trend-filter gap — and I can also add a maximum lot-size cap to the code so no single trade can ever balloon the way that $3,814.97 loss did.”

So what now,
I will strict my rules, in mq5, manage the risk per trade, manage the lot size and manage numbers of open trades.

Let’s see what happen, and I will keep doing this until i found a good profitable strategy, and will make its EA.

I have been working on many indicators since 2-3 years. All working well, I just thought if I can extend my hard work with few people who get all indicators in a single indicator. Of course it won't be free as tremendous hard work is all I've put in with lots of time. How much should I charge?

Please refrain from unnecessary comments.

Follow-up to the earlier RSI DCA posts (same logic I ran on POL, JUP, ETH). Last time I said I'd test whether a trailing exit could pull more out of this than the fixed 3% take-profit. I ran both on XRP — here's what came back.

Same long-only DCA: a selective deep-oversold 4h RSI entry and a scaling safety-order ladder, closed at a fixed take-profit above the blended average. Backtest is verifiable in TradingView's Strategy Report on the script page.

Fixed 3% TP vs trailing — the result: On this XRP window, at matched risk (~1.00% max drawdown either way), the fixed 3% TP came out clearly ahead: +3.45% net vs +1.85% net for the trailing variant. My read is that in the current chop-and-grind regime, deals close cleanly on the bounce and a trail mostly gives edge back to noise — the fixed target banks the snap-back before price wobbles. In a strong bull leg I'd expect the opposite: rebounds run further, and a fixed 3% leaves money on the table, so the trailing variant is worth testing there. Defaults ship with the fixed TP; the trailing inputs are exposed if you want to run the bull-market variant yourself.

Entry, deep-oversold gate (no repaint): A 4-hour RSI(14), sampled with lookahead disabled, gates the base entry — a long opens only when RSI prints below 28 at host-bar close. Shallow dips are filtered out.

Ladder, 5 safety orders on a non-uniform fixed-deviation ladder: Each safety order has its own fixed deviation from base entry. AO1 at −2%, AO2 at −5%, AO3 at −9.5%, AO4 at −16%, AO5 at −25%. Sizes scale 1.8× from a 900 USDT first AO: 900 / 1,620 / 2,916 / 5,249 / 9,448, on a 500 USDT base. The 1.8× progression is softer than a 2× doubling martingale — concentrates size in the deeper rungs but caps deployment lower.

Exit, fixed 3% TP: A fixed 3% Take Profit above the running average entry. Because the scaling ladder weights the average toward the lowest fills, after several rungs fill the average sits well below base — so a modest 3% bounce off the lows closes the whole deal in profit.

Risk, bounded ladder in place of a stop: No stop loss. Per-trade risk is structurally capped by the bounded 5-AO ladder — base + 5 AOs = ~20,633 USDT max deployed, ~20.6% of the default 100k equity, above the conventional 5–10% band; scale the inputs down to dial exposure lower.

DCA Bot integration: Every event (base, AO 1–5, exit) emits a webhook-ready JSON payload. One alert with "Any alert() function call" drives a DCA Bot end-to-end.

Backtest, fixed-TP version (BYBIT:XRPUSDT.P 4h, Jan 1 2024 – Jun 29 2026, ~30 months; 100,000 USDT initial capital, 500 USDT base + 900/1,620/2,916/5,249/9,448 AOs, 0.06% commission, 3-tick slippage): 75 closed trades, 57 profitable (76.00% WR), profit factor 13.058, net profit +3,450.27 USDT (+3.45%), max equity drawdown 1,010.83 USDT (1.00%). The trailing variant on the same window returned +1.85% net at the same ~1.00% max drawdown.

Methodology notes:
Read the numbers for what they are — a low-return, low-drawdown profile. +3.45% over ~30 months with 1.00% max drawdown is a risk-control profile, not a growth engine.

On sample size — 75 closed trades is below the ~100-trade floor for statistical confidence, the trade-off of a selective trigger. The 76.00% win rate and PF 13.058 are indicative of how the ladder behaves on this window, not a forward edge. And the very high PF is partly the averaging mechanic itself — deals close on a 3% bounce off an averaged-down entry — not a directional edge. The A/B above is a single-window comparison too, so treat "fixed beats trailing here" as a finding on this regime, not a universal rule.

This is a scaling martingale, and that's the dominant risk. The ladder bottoms at −25% with no stop loss — a sustained XRP decline below −25% without recovery leaves the full position open with no further averaging. The 1.00% max drawdown is closed-trade equity drawdown over a window where dips recovered; a deeper decline than the test sample would produce a larger one.

Defaults (RSI<28, 4h, the ladder, 1.8× sizing) are calibrated for XRP. With a fixed 3% TP the per-trade edge is modest, so match the 0.06% commission to your venue before reading into the numbers.

Strategy is open-source on TradingView: https://www.tradingview.com/script/dLdjnPLD-XRP-RSI-Strategy-3Commas/