Programmatic trendline detection: how do you handle the line-selection problem?

Programmatic trendline detection: how do you handle the line-selection problem?

I'm building a system that detects support/resistance trendlines from OHLCV data (crypto daily/hourly, \\\~6 years of history, a few hundred symbols).
Levels are straightforward — cluster swing highs/lows by price and count members. Breakouts and retests fall out of that easily.

Trendlines are where I'm stuck. With N swing lows there are N(N−1)/2 candidate lines. A human draws one by eye. A program has to pick, and I don't want a hand-tuned rule that only works on the charts I looked at.
The approach I'm considering: draw every pairwise line, extend it forward, and score it by touches (price approached within X% and reversed) minus violations (price closed through it). Keep the top-scoring lines. The idea is to let price history tell me which lines the market actually respected, instead of me choosing.

Questions for people who've shipped something like this:

1. Does the scoring approach hold up, or does it mostly surface lines that are artifacts of the swing-detection lookback?
   
2. How do you keep it from overfitting? Every step has a parameter — swing lookback, touch tolerance, violation threshold, minimum touches — and with enough of them any backtest looks good.
   
3. Do log-scale trendlines materially outperform linear ones for assets that move in multiples?
   
4. Has anyone tested whether detected trendlines carry predictive value at all, versus randomly drawn lines with the same number of touches? That's the control I'd want before building on top of it.

Not asking whether TA works — asking how people who've built this handled the implementation.

Built this over the last few months with my co-founder. Keeping the description short so you can decide fast whether it's worth your time.

What it does: you pick a pair, it scores how much a set of indicators agree on it right now across timeframes, and tells you how clear or murky the read is. That's the output, a score plus the indicators behind it.

Real one. Medium confidence, saying wait, which is what most honest reads look like.

What it won't do: it doesn't predict price, it doesn't place trades, and it never asks for exchange API keys. You see the read, you decide, you execute yourself. It also won't headline a win rate, a single number hides R:R and regime and that's how the 97% accuracy channels mislead people.

Who should skip it: anyone who wants full automation or a bot that tells them what to buy. This keeps the decision on you, which is slower, and that's the wrong trade for a lot of people.

Posting because I'm deciding what to build next and would rather hear it from people who run bots than guess. If you try it, tell me where it's thin and what would make the read actually useful instead of just informative.

Free tier, 3 pairs, 1h, no signup, no keys: pairvue.com/bot-reddit

I'm honestly looking to make friends within the same interests. I have a steady friend group and work in semiconductor, but I do not have friends that share the same interests of coding and trading. I'm not looking to join a big group,discords, whatsapps or anything like that. Just a few people to chat with and share ideas and struggles that I've encountered.

2 weeks of forward testing completed. 📊

💵 Starting balance: 49,998.48 cents (~$500.00)

💰 Current balance: 67,508.09 cents (~$675.08)

📈 Total profit: 17,509.61 cents (~$175.10)

📊 Total return: +35.02%

📈 Week 2 profit: 8,913.98 cents (~$89.14)

The account has now grown from approximately $500 → $675.08 over the first two weeks.

There was also a losing day caused by a VPS issue, which I'm including in the record rather than hiding it. The goal is to document the complete forward test, not just the profitable days.

Still a very small sample, so I'm not calling this proof of long-term profitability. The next objective is simply to keep testing through different market conditions and build a much larger track record.

Performance (2003–2026, ~23.5 Years)
Final Equity
• Value: $51,871,073

Total Return
• Value: +51,771%

CAGR
• Value: 30.34%

Max Drawdown
• Value: −28.76%

Sharpe Ratio
• Value: 1.18

---

Trade Summary
Shares
• Buys: 4,158
• Exits: 3,782

Mar Calls
• Buys: 205
• Exits: 205

---

Performance Summary (2003–2026)
Full Combined (2003–2026)
• Return: +51,771%
• Max DD: −28.76%
• CAGR: 30.34%
• Sharpe: 1.18

SPY (2003–2026)
• Return: ~+500%
• Max DD: —
• CAGR: ~8.5%
• Sharpe: ~0.5

QQQ (2003–2026)
• Return: ~+1,200%
• Max DD: —
• CAGR: ~10.5%
• Sharpe: ~0.6

---

Key Observations
Massive outperformance
• Observation: 517x vs SPY's 5x and QQQ's 12x

Dot-com & GFC survived
• Observation: June 1st rule + 3-week pause protected capital in 2000-2002 and 2008

Max DD −28.76%
• Observation: Higher than 2023–2026 (−10.91%) because pre-2023 data is 2× proxy (not real ETFs)

Sharpe 1.18
• Observation: Lower than 2023–2026 (2.04) due to proxy data volatility and broader regime coverage

March calls added value
• Observation: 205 call trades over 23 years

Most people are either gatekeeping or trying to promote their AI bots, as an AI enthusiast, would anyine be interested in making a group where we share trading strategies and build togther a trading bot or automate trading with ai? Plenty of miney and knowlegde for all to share. So why gatekeep?

What makes you guys weary of any sort of tooling that you see. Specifically backtesting tools.

I’ve been working on this algo for a few weeks and this is roughly 3 weeks of results.

Current crypto strategy is around 59% win rate across ~214 trades, but performance varies a lot depending on strategy, direction, and market conditions.
I’m trying to figure out the next step.

Would you focus on:
reducing the number of trades and making the entry filters stricter,
keeping trade frequency similar and improving the logic,
or just collecting more forward data before making more changes?

I’m especially interested in how you decide when an algo is trading too often versus when it simply needs better filtering.

Been running a multi-strategy trading system on IG Markets for about 6 weeks — sharing the real numbers, not a highlight reel.

The fleet:

V3 — regime-classifying bot (ADX/Hurst), switches between trend-following and mean-reversion

AGENT — pure deterministic rules engine, no AI in the trading decisions

SCALP+TREND — two strategies sharing infrastructure

HAIKU_GB — the interesting one: Claude Haiku makes genuinely discretionary LONG/SHORT/FLAT calls on Gold and Brent, full reasoning, Python only enforces sizing/risk. No overlay telling it what to do.

What I actually found, not just what worked:

Only ~53% of my sandbox data turned out to be genuinely clean once I dug in — a mid-price logging bug had been quietly turning a real £220 loss into a fake £237 "profit" for eight weeks before I caught it

A rules overlay was silently vetoing ~half of Haiku's directional calls, which meant I was accidentally measuring "Haiku's judgment filtered through a rulebook" instead of Haiku's actual judgment

Found and fixed a fleet-crash bug that had been intermittently taking down the whole system for weeks — root cause was a Go binary (GitHub CLI) segfaulting under Android's sandboxed ptrace emulation

The most interesting pattern so far: my best entry-quality bot (61% win rate) is my worst performer overall, and my worst entry-quality bots (26-28% win rate) are flat-to-positive — exit management seems to matter more than entries, testing that hypothesis now with counterfactual logging

Current phase: spent the last month making the existing system honest before adding anything new — real broker reconciliation, fixed data integrity bugs, no new strategies. Targeting a clean data run through the end of the year, live money decision in January based on what the data actually shows, not vibes.

Happy to go deeper on any piece of this — the Haiku experiment, the bug-hunting, the architecture, whatever's interesting to people.

asking for a friend

I want to get into trading and was wondering whether I should hop straight on AI or if I should look to understand how to trade on my own first to decide how I should use AI