ICT Order Block + FVG strategy in Pine Script v5: backtest results + full code

isabelle dubuis — Thu, 30 Apr 2026 21:31:08 +0000

I've been backtesting an ICT-style Order Block + Fair Value Gap strategy on crypto and forex. After 50 iterations and a lot of dead variants, here's what survived. Posting the Pine Script v5 below in case it saves someone the same week of debugging.

The setup

ICT (Inner Circle Trader) methodology relies on two ideas:

Order Block (OB) — the last opposite candle right before a strong displacement. Bull OB = last bearish candle before a sharp bullish push.
Fair Value Gap (FVG) — a 3-bar imbalance where price moved so fast it left a gap (low > high[2]).

Entry = price retraces back into the OB, ideally inside an FVG, with trend confirmation.

Why this version finally worked

Earlier tries took every OB. Win rate dropped to 35%. Two filters fixed it:

Displacement filter: the move out of the OB must exceed 2 × ATR(14). Weak moves create weak OBs. They don't hold.
Trend filter: only longs above EMA(200) trending up. Short below, trending down. Sounds basic, but cut my losing trades by 40%.

Stop loss = OB low minus 0.3 × ATR. TP = 3R fixed. Anything tighter and noise eats you. Anything wider and you give back too much.

The full Pine Script v5

//@version=5
strategy("ICT_OB_SEED11", overlay=true, initial_capital=3000, commission_type=strategy.commission.percent, commission_value=0.05, slippage=2, default_qty_type=strategy.cash, default_qty_value=300, process_orders_on_close=true)

displ_atr_mult = input.float(2.0, "Displacement ATR mult")
ob_lookback    = input.int(20, "OB lookback")

atr = ta.atr(14)
displ_up = close - close[1] > atr * displ_atr_mult
displ_dn = close[1] - close > atr * displ_atr_mult

var float obUpHi = na, var float obUpLo = na
var float obDnHi = na, var float obDnLo = na

if displ_up and close[1] < open[1]
    obUpHi := high[1]
    obUpLo := low[1]
if displ_dn and close[1] > open[1]
    obDnHi := high[1]
    obDnLo := low[1]

bullFVG = low > high[2]
bearFVG = high < low[2]

ema200 = ta.ema(close, 200)
upTrend = close > ema200 and ema200 > ema200[20]
dnTrend = close < ema200 and ema200 < ema200[20]

retestLong  = upTrend and not na(obUpHi) and low <= obUpHi and high >= obUpLo and close > open
retestShort = dnTrend and not na(obDnLo) and high >= obDnLo and low <= obDnHi and close < open

if retestLong and barstate.isconfirmed and strategy.position_size == 0
    sl = obUpLo - atr * 0.3
    tp = close + (close - sl) * 3
    strategy.entry("L", strategy.long)
    strategy.exit("XL", "L", stop=sl, limit=tp)
    obUpHi := na
    obUpLo := na

if retestShort and barstate.isconfirmed and strategy.position_size == 0
    sl = obDnHi + atr * 0.3
    tp = close - (sl - close) * 3
    strategy.entry("S", strategy.short)
    strategy.exit("XS", "S", stop=sl, limit=tp)
    obDnHi := na
    obDnLo := na

Results across pairs (1H, 2 years backtest)

BTCUSDT: PF 1.42, MaxDD -8.2%, 312 trades
EURUSD: PF 1.18, MaxDD -6.1%, 184 trades
XAUUSD: PF 1.61, MaxDD -11.4%, 247 trades
SP500 1H: PF 1.09, MaxDD -7.8%, 142 trades

PF below 1.1 = skip. Above 1.4 = keep monitoring forward. Forward test always trails backtest by 15-25%.

What I tried that didn't work

ATR(7) instead of ATR(14): too noisy, false displacements
Tighter SL (0.1 ATR): stopped out 70% of valid setups
5R TP: too greedy, only 12% hit it before reversal
No trend filter: half the OBs bounced once then died

Next iterations

Add session filter (London/NY only, skip Asia)
Multi-timeframe OB confirmation (4H OB on 1H entry)
Volume profile overlay for OB validation

About me

I run a small portfolio of niche tools and content sites across AI, SEO, trading, and B2B. Some of them:

AI & automation:

vocalis.pro — AI-powered SEO content automation
vocalis.blog — case studies and field notes
vocalis-ai.org — voice AI agents for sales calls
agents-ia.pro — agentic AI playbook
agentic-whatsup.com — agentic AI for WhatsApp business
ai-due.com — AI for SMB operations

SEO & sales:

seo-true.com — SEO automation toolkit
master-seller.fr — sales enablement (FR)
lead-gene.com — B2B lead generation tools

Trust, security, finance:

trustly-ai.com — trust signals automation
trust-vault.com — encrypted vault tools
factureimpayee.fr — French unpaid invoice recovery

Niche content:

tesla-mag.ch — Tesla coverage (CH)
iapmesuisse.ch — AI for Swiss SMBs
cbdeuropa.com — CBD market analysis EU

If you trade and want to chat about backtesting frameworks or automation in general, drop a comment. Anyone running ICT on lower timeframes (5m/15m)? Curious what your filter stack looks like.

I built a Reddit posting bot. Account got banned in 2h. Honest scorecard.

isabelle dubuis — Thu, 30 Apr 2026 20:28:05 +0000

I built a Reddit posting bot for my SEO agency. Here's the honest scorecard.

Built a Reddit posting bot for my SEO agency. Goal: stop spending 5h/week on social presence busywork.

Final stack after rewrites

Apify scans subs, scores threads on intent + age + engagement
OpenAI gpt-4o-mini drafts posts and comments (cheap, ~$0.002/draft)
Playwright on old.reddit.com submits (new Reddit UI fights you on every selector)
JSON files for state. No DB, no queue, no Docker.

What worked

Comments landed from day 1. Posted on r/SEO, got real replies.
Cooldown logic (7d/sub) stopped me from spamming the same place twice.
Found 13 safe subs by scraping each sub's about.json + rules.json. Filter out karma minimums and anything over 1M subs (heavy automod).

What broke

First 3 self-posts got nuked silently by AutoModerator. Account was 3 months old, 1 karma, email unverified. Reddit's spam classifier reads account age x karma x verified flags before it reads content.
My success detection looked at page URL after submit. Removed posts return a normal URL, so the script logged success while Reddit deleted in the background.
The LLM hallucinated tech terms not in the OP. Temperature 0.7 to 0.6 helped, stricter prompt helped more.

What killed the account

After ~2h of automated runs from a datacenter IP (OVH), Reddit shadowbanned the account. Anti-bot detection picked up: datacenter IP + Playwright fingerprint + 4 tabs opened simultaneously + frappes synthétiques + activity 24/7 cron pattern.

Total cost

14 days, ~30 hours coding, 4 restarts
$4 OpenAI, $7 Apify
Zero hours saved (account got banned before karma built)

The real lesson

You cannot automate past Reddit's reputation system. CQS (Contributor Quality Score) is the hidden gate. Build karma the slow human way first via mobile + residential IP, then automation works on accounts with 200+ karma after 60 days.

Anyone else hit the wall where automation works perfectly but the platform silently shadowbans the output?

I built vocalis.pro — an AI-powered SEO content automation tool. The bot from this story is one of its modules. If you want to skip the 30 hours of pain, check it out.

DEV Community: isabelle dubuis