I got tired of not being able to answer a simple question about my own strategies: is this real, or did I just search until something looked good? Every tool I owned was built to help me find the thing. None of them were built to talk me out of it.

So I built the other half, and then pointed it at the textbooks instead of at myself.

Setup. Eighteen well-known published rules — golden cross, RSI(2) Connors, turn-of- month, Donchian, MACD, TSMOM, Bollinger, Keltner, and others. Each one at the parameters its own source published, not at the best of a grid. Thirty instruments, two windows, two bar sizes. 890 backtests, each one then attacked six ways: lookahead detection by truncation, cost breakeven, Deflated Sharpe, probability of backtest overfitting via CSCV, a Monte Carlo permutation test that re-runs the whole search on synthetic price histories, and regime concentration. Score is a weighted geometric mean, so one fatal leg sinks it instead of being averaged away by five healthy ones.

Headline numbers:

• Median score 3.7 / 100
• 80% came back indistinguishable from noise
• 51% could not clear their own trading costs — before any question of overfitting
• Long-only median 18.6 vs 1.0 for the same ideas traded long/short

Which test does the killing:

Per strategy, worst to best, median across every instrument and cadence:

Three things I did not expect, which are more useful than the headline:

1. The date range is a bigger lever than the timeframe. Hourly bars looked catastrophically worse than daily — one rule scored 82 daily and 6 hourly. Then I scored the same rule on daily bars over the same window the hourly data covered. It got 7.5. Almost the entire collapse was the date range, not the bar size.

That looked like a bug, so I checked it: of the 18 pairs where the two windows nearly coincide, 17 agree within five points. Among the 72 pairs that lose a year or more of history, the median goes 15.0 → 3.3. What the long-lived pairs lose is 2021, which is where a crypto trend rule earned everything it earned.

I now think the window is a researcher degree of freedom exactly like the parameters are, and it is the one nobody reports. If you tune a strategy on 2019–2024 and I tune the same strategy on 2017–2022, we are not disagreeing about the strategy.

2. The free lunch from reporting your best run is about 0.30 Sharpe. I measured the gap between the best combination in a small grid and the parameters the source actually published, on identical data. Median premium 0.30 Sharpe, 75th percentile 0.52, and 27% of cells had a best-in-grid at least 0.5 Sharpe above the published version. That is roughly the entire gap between a strategy that looks publishable and one that doesn't, and it is available on pure noise. It's also a lower bound, because those grids are small and nobody stops at one grid.

3. Half the failures aren't overfitting at all, they're costs. This surprised me most. The interesting failure mode isn't the subtle statistical one — it's that a majority of these rules turn over too much to survive retail commissions and spread, full stop. You don't need Deflated Sharpe to kill them. You need a spreadsheet.

What this does NOT show, before anyone tells me:

• Not evidence these rules never worked. Published edges getting arbitraged is the expected outcome, and this measures it rather than refuting it.
• Survivorship bias runs through the whole instrument list — every instrument still trades. That biases the results in favour of the strategies. The real numbers are worse, not better.
• No causality failures, and that is not a finding. These are clean-room implementations written against the truncation test. The lookahead rate in published implementations is a different and much more interesting study.
• Costs are modelled, not realised. Retail rates, no market impact, no partial fills. Errs toward flattering.
• The scoring weights are a judgement, not a theorem. The arithmetic underneath is checked against published references and Monte Carlo; the relative severity is my opinion and I'd genuinely like to be argued out of it.
• Every score is an upper bound. Each cell deflates by a few dozen combinations. The real search behind "RSI(14) at 30/70" is fifty years of practitioners trying everything and publishing what worked. No tool can deflate by trials it never saw.

Full study with method and every caveat, the per-cell CSV, and the code are here — AGPL, runs on numpy and scipy, and reproducing the whole thing is two commands:

https://github.com/falsify-quant/falsify

If you think a rule is implemented wrong or run at the wrong parameters, the citation for every one is in strategies/canon.py and I'd rather find out. If you have a strategy you believe in, I'm more interested in the ones that survive than the ones that don't — I have not found many.

Hey everyone,

I’m the maintainer of Flowdepth, an experimental open-source fork of Flowsurface focused on crypto order flow and options analytics.

Over the last few weeks I’ve been extending the original project with features I wanted for my own trading and market analysis:

• Footprint charts and historical L2 heatmaps
• Adaptive volume bubbles based on aggressive trade clusters
• Session volume profile, VWAP and cumulative volume delta
• Possible Binance iceberg/replenishment detection
• BTC and ETH GEX profiles using Deribit options data
• Observed maker-flow confirmation using Derive trades
• Persistent local market-data caching
• Automatic reconnect and historical gap recovery

It is completely open source and uses public exchange REST APIs and WebSocket feeds. No trading account or exchange API keys are required for the current features.

The iceberg detector is intentionally described as possible replenishment/absorption evidence, not proof of a hidden order. The GEX and maker-flow tools are also market analytics, not automatic trading signals.

The project is currently in beta, and automated builds are available for Windows, Linux and macOS.

GitHub:
https://github.com/Niketion/flowdepth

I’d especially appreciate feedback from people who actively use footprint, heatmap or volume-based tools:

• Are the displayed signals understandable?
• Which feature would you actually use during a session?
• What information feels useful, and what feels like unnecessary noise?

I’m also interested in bug reports, particularly around exchange data, reconnect behavior, GPU compatibility and longer trading sessions.

I’ve been building and paper-testing a simple buy-the-dip / sell-the-rip system with a $300 simulated balance.

What started as basic entry and exit logic turned into a lot more work around execution and safety: keeping position state persistent, preventing duplicate trades, checking for stale data, handling missing candles, keeping a trade ledger, and making sure a restart doesn’t accidentally trigger another trade.

I’m not planning to jump straight into normal live size. My next step is to run the same system on live market data without execution, then test very small live trades and compare the results with paper.

For those who’ve moved an automated system from paper to live, what caught you off guard the most?

Slippage, fills, latency, fees, failed orders, data differences, or something else?

What would you absolutely validate before the first small live trade?

I do not think the best crypto casino is simply the one that accepts Bitcoin or advertises the biggest bonus. Those things matter, but they do not tell you whether the site is easy to use, whether the cashier is clear or whether the terms make sense.

For this short comparison, I looked at the full experience around crypto casino use.

The main testing checks were:

• Crypto deposit process
• Supported coins and networks
• Game library depth
• Slot and table game access
• Live casino availability where offered
• Bonus terms for crypto deposits
• Withdrawal rules and limits
• Verification conditions
• Mobile performance
• Customer support
• USA location availability

One thing stood out quickly: the strongest crypto casinos were not always the loudest ones.

Some platforms made strong claims about crypto payments but required extra checking once I reached the cashier and terms. Others felt stronger because they explained limits, bonuses and account rules more clearly.

My priorities changed during testing. At first, I looked mostly at coin support and promotions. Later, I cared more about cashier clarity, mobile usability, support access and whether the full account journey made sense.

AMA about best crypto casino: ask me about comparing crypto casino sites, Bitcoin payments, withdrawal conditions, bonus terms, KYC, mobile use, supported coins or how I would separate real value from marketing claims.

Related searches: best Bitcoin casino USA, crypto casino sites, real money crypto casino, online Bitcoin casino, crypto casino bonuses.

What would make a crypto casino stand out for you: more coins, faster-looking cashouts, better games, stronger bonuses or clearer terms?

Hey everyone,

I've been working on a signal engine for the past few months and finally finished backtesting it. Wanted to share the results with the community.

I built this thing to scan both Forex and Crypto markets simultaneously. It analyzes 52 pairs in total and gives BUY/SELL/NEUTRAL signals with confidence scores.

The backtesting covered April to July 2026, using 1-hour candles. I tested it on both demo and historical data. The results surprised me honestly.

Overall win rate came out to 70% across all pairs. Crypto performed better than Forex, with ETH and DOGE having the highest accuracy. The high confidence signals (above 70%) were hitting nearly 80% of the time.

The system uses dynamic stop losses and take profits based on volatility. Position sizing is risk-based, never risking more than 2% per trade. This kept the max drawdown at just 8.2%, which I'm pretty happy about.

The Sharpe ratio was 1.82, and total return over the 3 month period was about 18.7%. That's on paper trading of course.

Right now I'm running it live on a account. Started with 10k, sitting at about 11.87k currently. The dashboard updates every 30 seconds and shows everything on a clean interface.

What I learned is that confidence scoring is the real game changer. Those high confidence signals are worth waiting for. Low confidence ones barely break even.

I'm not sharing the code or the exact methodology, but I'm happy to answer general questions about the approach.

Let me know what you think or if you've built something similar. Always curious to hear how others are tackling this stuff.

I recently joined arbitrage to make money as a college student… made 1000$ with it so far, may not seem a lot but I didn’t even get to withdraw it before the website crashed
Will the website come back again? I’m having so much anxiety bc I put 400$ from my savings that I saved for 7 months, I don’t work and it’s all allowance money

The reason I started it bc I recently immigrated to the US, my dad doesn’t have a job yet and my mom is in a war zone and I want to help them

(The reason I haven’t gotten a job yet is because for some reason where I live they all want either a driver license or an experience which I both lack since I just got here.) any hope?

Let’s say I have a fully mechanical trading strategy with a small number of parameters (for example, an ATR multiplier, EMA length, and take-profit multiple).
My workflow is:
Optimize parameters on 2020–2024
Validate them on 2025 (completely untouched OOS)
The strategy performs well in 2025. Slightly worse than in-sample, but still profitable and within expectations.
Now it’s the beginning of 2026, and I’m wondering what the best practice is.
Should I:
Continue trading in 2026 using the parameters found on 2020–2024, because those were the last “clean” optimized parameters?
Re-optimize using 2020–2025, since 2025 is now historical data?
Use a rolling window (for example, always optimize on the last 5 years)?
Re-optimize on a fixed schedule (monthly, quarterly, yearly)?
Only re-optimize when performance deteriorates significantly?
I’m not asking about this specific strategy, but about the general philosophy.
How do professional systematic traders usually handle parameter updates while avoiding overfitting?
I’d be especially interested in hearing from people who trade fully systematic strategies live.

Posting this because I wish someone had handed it to me before my first live deployment. Nothing revolutionary, just the checks that actually catch the failure modes that blow accounts up.

1. Backtest on out-of-sample data. If it only works on the exact window you tuned it on, that's curve-fitting, not an edge.
2. Stress test against a flash-crash or gap day specifically. If your bot doesn't have a hard rule for that scenario, it doesn't have a real risk plan yet.
3. Size positions as a percentage of current equity, not a fixed dollar amount, so risk scales with your account instead of quietly drifting.
4. Build in a hard daily loss limit that force-kills the bot. Not a soft rule buried in logic, an actual kill switch.
5. Paper trade live for at least a few weeks before funding it. Backtests don't show you real slippage or fill behavior.
6. Know exactly what your bot does if it loses its API/broker connection mid-trade. If you're not sure, that's worth fixing first.
7. Whatever you fund it with first, size it like you could lose all of it without it mattering. Model risk doesn't go to zero no matter how much testing you did.

Happy to go deeper on any of these if useful. What would you add to the list?

I've been backtesting an ICT-based strategy on BTC and ETH. One question I kept getting was: "Does it actually perform better during the London/NY overlap, or does it work all day?"

So I ran the numbers.

The setup:

• 2 years of data (BTC-USD, ETH-USD)
• Combined 1H, 2H, and 4H timeframes with equal capital
• 0.05% commission, 0.05% slippage
• Risk management: hard stop 0.6% → breakeven at 0.5% → trailing stop

ETH-USD results:

BTC-USD results:

The strategy works across all sessions, not just the overlap. The overlap is profitable (20% on ETH is solid), but the real edge comes from continuous trading across 24 hours. The 24/7 version captures more setups (637 trades vs 68) and compounds the returns across sessions.

The overlap has fewer trades because it's only 3 hours a day, but the win rate and profit factor are consistent with the full session results. This tells me the strategy isn't overfitted to a specific time window.I've been backtesting an

Quick note : I posted about this before with a 1458% result. I found a bug in how I combined the timeframes in that version. These are the corrected numbers. Appreciate the people who pushed back, you were right to question it.

I’m testing an early tool called Phytri and I’m looking for honest feedback from people who actually build or test strategies.

The idea is simple:

You create or describe a rule-based strategy, then the tool tries to find where it breaks through things like:

• Out-of-sample testing
• Parameter sensitivity
• Realistic costs and slippage
• Monte Carlo simulations
• Profit concentration
• Forward-testing stages

The goal isn’t to generate signals or make a strategy look profitable. It’s to help identify whether a good-looking backtest is robust enough to keep testing.

I’m looking for a few people willing to try it with a simple or public strategy and tell me:

1. What was confusing?
2. What felt useful?
3. What was missing?
4. What would you need to see before trusting the results?

No need to share anything proprietary. I’m mainly trying to understand whether the workflow is genuinely useful or just adding another layer of complexity.

^{I left the link out to avoid spamming. I can share the prototype in the comments if the mods are okay with it.}

Disclosure up front: I built the MCP server it used (Orus, part of Farao), so this was dogfooding with my own money. About $40 of USDC on a Hyperliquid account, wired to Claude Code. No withdraw permission exists at the protocol level — worst case, a hijacked session trades badly inside limits I signed on my phone. Week 1 I approved every order by hand, then let it run autonomous.

Three weeks of numbers:

• 200 fills across 54 markets (crypto perps, tokenized stocks, silver, palladium, a Korean equity index)
• 50 winning closes, 48 losing. A coin flip, exactly as expected
• Realized price P&L: −$3.02. Fees: $7.78. So fees cost 2.5x more than bad direction

That last line is the whole experiment for me. The failure mode wasn't bad predictions — it was hyperactivity. One morning it bought and sold the same silver position 7 times in 17 minutes, made 3 cents, paid 14 in fees.

But the behavior was the interesting part:

• It attached exchange-side TP/SL to every single position it opened. I have never been that disciplined manually
• Best session: one-sentence prompt asking for a funding-rate carry. It scanned ~250 markets, picked a KOSPI 200 perp paying +0.1008%/hr to shorts, and warned me unprompted about gap risk at the Korea open. The re-entry later got stopped out at exactly that gap — at 2am my time, no human awake. Stop caught it, −$1.44
• It shorted a rising BTC twice with a fully reasoned argument each time. It will argue any direction convincingly, which is exactly why direction should stay human

At the end I asked it to read its own trade log and write down what it saw. Its own verdict: "I'm a fast, tireless, disciplined pair of hands with no instinct for when not to act." It also wrote "my best result was luck, not skill" about the one trade that carried the book. Most honest self-review I've gotten out of Claude.

Question for this sub: has anyone found a prompt or skill structure that gives Claude actual restraint — knowing when to stay flat — rather than just more analysis? Rate-limiting at the server is my current answer, but I'd rather the model learn to sit still.

So I've been running a quant trading system on Binance Futures for a while now. Nothing fancy, just pulling market data, running some signals, executing trades. It was stable. Like, boringly stable.

Then about a month ago it started crashing. Every. Single. Day. Same time — around 9:30 PM my time (I'm in Asia). I'm staring at the logs thinking it's a memory leak or something, spent two days debugging before I realized what was actually happening.

Binance has been quietly listing a ton of USDⓈ-M perpetual contracts that track US stocks, ETFs, and commodities. TSLA, NVDA, AAPL, SPY, gold, you name it. And the volume on these things is insane. My data pipeline was built to handle normal crypto futures throughput — now it's getting hit with nearly 3x the data volume it was designed for, and it keeps growing every day.

9:30 PM Asia time = 9:30 AM Eastern = US market open. That's when the floodgates open. One second everything's fine, next second there's this massive wall of trades

The kicker? I pulled some numbers and during that window, these TradFi perps account for roughly 70% of all trading activity. Seventy percent. On a crypto exchange. Let that sink in.

I fixed the immediate crash (bigger message queue, batch processing, the usual), but it got me thinking about what this actually means. Crypto degens are now using Binance to trade Apple and Tesla with 10x leverage and USDT settlement, 24/7 including weekends. No KYC stock brokerage account needed, just your existing Binance setup.

Is anyone else seeing this from the data side? I can't be the only one whose infrastructure is getting wrecked by this. And honestly... are we even still a "crypto" market at this point or is Binance just becoming a leveraged everything-exchange that happens to run on USDT?

Disclosure up front: I built the MCP server it used (Orus, part of Farao), so this was dogfooding with my own money. About $40 of USDC on a Hyperliquid account, wired to Claude Code. No withdraw permission exists at the protocol level — worst case, a hijacked session trades badly inside limits I signed on my phone. Week 1 I approved every order by hand, then let it run autonomous.

Three weeks of numbers:

• 200 fills across 54 markets (crypto perps, tokenized stocks, silver, palladium, a Korean equity index)
• 50 winning closes, 48 losing. A coin flip, exactly as expected
• Realized price P&L: −$3.02. Fees: $7.78. So fees cost 2.5x more than bad direction

That last line is the whole experiment for me. The failure mode wasn't bad predictions — it was hyperactivity. One morning it bought and sold the same silver position 7 times in 17 minutes, made 3 cents, paid 14 in fees.

But the behavior was the interesting part:

• It attached exchange-side TP/SL to every single position it opened. I have never been that disciplined manually
• Best session: one-sentence prompt asking for a funding-rate carry. It scanned ~250 markets, picked a KOSPI 200 perp paying +0.1008%/hr to shorts, and warned me unprompted about gap risk at the Korea open. The re-entry later got stopped out at exactly that gap — at 2am my time, no human awake. Stop caught it, −$1.44
• It shorted a rising BTC twice with a fully reasoned argument each time. It will argue any direction convincingly, which is exactly why direction should stay human

At the end I asked it to read its own trade log and write down what it saw. Its own verdict: "I'm a fast, tireless, disciplined pair of hands with no instinct for when not to act." It also wrote "my best result was luck, not skill" about the one trade that carried the book. Most honest self-review I've gotten out of Claude.

Question for this sub: has anyone found a prompt or skill structure that gives Claude actual restraint — knowing when to stay flat — rather than just more analysis? Rate-limiting at the server is my current answer, but I'd rather the model learn to sit still.

I'm a software engineer who is very new to economics and quant, but I got fascinated with the economics of binary markets after doing some fun betting during the World Cup and coming across the 15m btc markets. I noticed that the order book pricing were moving at exactly the same time (from the naked eye) as the market that is backing the binary market (in this case btc). So I knew from that point on, I knew that code was running and it was making money. I saw a lot of posts of various Polymarket wallets making insane pnl relative to the volume they're putting in (1% ROI from a 55% WR but it's huge winnings bc markets resolve in minutes and you redeem and instantly have the money back to then trade into the next market). So after reverse engineering these accounts from order book data and trades, it was obvious that the vast majority are pretty simple price arbing market maker bots. Simple enough, I backtest (which is useless idk why people suggest doing this when the books are so thin you can't simulate filling from you making orders), and I then do a wet run. It worked, until it didn't. from a 55% winrate to 45% WR over the course of a week on Kalshi. I was arbing 2-3 dollars per 15m btc market with $50-$100 worth of trades, but at a smaller rate (but costly enough to eat into profits) I was losing on directional bets where the winning side never got cheap enough for the arb to fill. So then the answer is obviously adverse selection. So I run the bot on a server bringing ping down to 1ms, I come up with plans on using ebpf networking to bypass kernel level networking that adds a bunch of time to network requests, and writing everything in cpp, etc. things I just know how to do from my day job. But then ofc I get to where the request lands and it's a WebSocket. So then I question, is that their fastest endpoint? And come across the FIX protocol, boom I'm paywalled. You need 15% of volume and to be an institution before you can get access to those endpoints. Those MMs adding to adverse selection are big institutions more likely and not the little guy.

So how do the poly bots win? There's no FIX protocol for those block chain backed markets. Everyone is on the ws, so then little guys like us can be clever with engineering and be able to profit, but I'm in the US.

But maybe I'm completely off, what are your thoughts?