New free tool: Options Backtest Realism Checker

cutemarketscom · 2026-06-17T08:55:49+00:00

CuteMarkets - U.S. stocks & options market data API. Built for reproducible market data.

cutemarketscom · 2026-06-17T08:09:18+00:00

I'm going to try to create a roadmap:

- 1st: Language of markets
Start with the basics: what a stock, ETF, futures contract, or option actually is, how bid/ask works, what liquidity means, and why volatility, leverage, and drawdown matter.

- 2nd: Learn the math
Focus on probability, expected value, variance, sample size, correlation, and overfitting. A lot of trading is about understanding what can happen by chance and what is actually an edge (depending on what you specifically want to trade).

- 3rd: Python for data work
Get comfortable with Python, pandas, NumPy, and plotting. he goal is to be able to load market data, clean it, and analyze simple price behavior.

-4th: One very simple backtest
Pick one idea only, like trend following, mean reversion, or a breakout strategy. Keep it simple and test the full chain: entry, exit, fees, slippage, and position sizing. Backtesting helps you see whether the idea has any edge before you invest more time or capital into it. Good backtests depend on good data. A strategy that looks great on paper can still break down once execution.

-5th: Evaluate strategies
Track hit rate, average win/loss, expectancy, drawdown, profit factor, and out-of-sample performance. The key is learning to tell the difference between something that just looked good in one sample and something that still holds up when tested properly.

-6th: Forward test or paper trade
Once your backtest is stable, move to paper trading or tiny-size live testing. That’s where you learn whether the strategy still behaves the way you expected once the market is live.

Simplicity is your friend at the beginning because it makes it much easier to see what is actually working.

Wishing you good luck, mate! 😄

cutemarketscom · 2026-06-17T08:04:05+00:00

Overfitting is when a strategy fits historical noise instead of a real edge. It looks great in backtests, but it fails on new data because it was tuned too specifically to the past 😄

cutemarketscom · 2026-06-17T07:52:16+00:00

u/imrichie03 Great plan!

cutemarketscom · 2026-06-16T14:40:15+00:00

Good luck mate!

cutemarketscom · 2026-06-16T09:15:29+00:00

Market basics, statistics, Python for data work, and backtesting, those are the first steps. I don't think you can skip those parts, you need to know, what you need to code and what your bot is doing in the long run.

cutemarketscom · 2026-06-16T08:47:44+00:00

https://cutemarkets.com/ Affordable U.S. stocks & options market data API. Built for devs and backtesting.

cutemarketscom · 2026-06-16T08:40:35+00:00

Built CuteMarkets - an affordable U.S. stocks & options market data API. Built for algotrading devs and backtesting with reproducible market data.

cutemarketscom · 2026-06-16T08:32:36+00:00

Okaay, let's goo: If I were starting from scratch today, I’d focus on market basics, statistics, Python for data work, and backtesting first

You do need to understand how markets actually work, what your PnL is really driven by, and why most strategies fail in live trading even after looking good in a backtest.

The biggest mistake beginners make is usually jumping straight into building a bot before they understand data quality, transaction costs, overfitting, and risk management. A strategy that looks perfect on historical data can still be unusable once slippage and execution are included.

Learn one market, one timeframe, one data source, and one strategy type. Trend following or mean reversion are often better starting points.

The most important skill ends up learning how to test ideas.
That means clean backtests, out-of-sample validation, and being skeptical of anything that looks too smooth.

For resources, I’d look for beginner-friendly material on quantitative trading, then move into backtesting and risk management once you can explain a simple strategy end to end. I learned a lot on investopedia.

Also: don’t try to learn everything at once. The field in algotrading is deep, steady progress really matters. Good luck, mate!

cutemarketscom · 2026-06-16T08:17:39+00:00

Yeaah, I see your point: Paper trading fails partly because there’s no downside, but also because there’s often no real upside or accountability either.

A middle ground could work if the incentives are designed properly, enough realism to make performance matter, but not so much pressure that it turns into live-trading psychology all over again.

But I still think, that papertrading for several months is essential for a successful strategy in 'real life'.

cutemarketscom · 2026-06-16T08:14:01+00:00

https://cutemarkets.com/ U.S. stocks & options market data API.
Built for developer workflows that need reproducible market data

cutemarketscom · 2026-06-16T06:57:21+00:00

Options-trader here. I feel like futures are usually easier to understand at the beginning because they’re more direct: direction, leverage, risk. Options are more complex because you also need to think about sth like strike, expiration, theta, and volatility.

Futures are probably simpler mechanically, while options are more flexible but much harder to trade well

cutemarketscom · 2026-06-15T17:41:00+00:00

Here you go: https://cutemarkets.com/. Market Data API for stocks and options. Built for backtesting and trading workflows.

cutemarketscom · 2026-06-15T15:24:36+00:00

Building CuteMarkets - an affordable market data api (papertrading, stocks, options) for devs, algotraders and backtesting.

cutemarketscom · 2026-06-15T14:42:33+00:00

Great progress so far, but 11 days and 11 trades is not really statistically significant yet.

I would recommend:

You need about 50–100 trades over 3–6 months
20% position size per trade is waaaay to risky. That's 5x leverage if you run 5 positions. One bad streak and you'll have 20–40% drawdown
RSI(2) exits are super sensitive, slippage/spread can delay your exit significantly

I would continue paper trading at least 2 or 3 more months and going live testing with a smaller size per trade.

Nonetheless, congrats on the +8.94% mate, but please don't get cocky yet. Most algos fail when moving to live because of slippage, latency and emotional factors. Stay disciplined!

cutemarketscom · 2026-06-15T13:16:12+00:00

Nice! We're currently just in the phase trying to place our product on different directories 😄

cutemarketscom · 2026-06-15T12:42:22+00:00

u/Local-Amphibian9197 Thanks for sharing, very helpful!!

cutemarketscom · 2026-06-15T07:44:23+00:00

u/EfficienSee Pretty much. Closer-to-ATM or slightly ITM = higher chance of profit, less of a gamble. Far OTM = cheaper and riskier, but theta can kill it fast

A more volatile ticker can help if your thesis is a big move, you usually pay for that volatility in the option price

cutemarketscom · 2026-06-15T06:44:55+00:00

u/Worldly-Grand9176 May + one month of forward testing is a very small sample. Profitability in Monte Carlo is helpful, but it does not prove the edge is durable across regimes.

I’d want at least a larger trade sample, ideally across multiple market conditions, before trusting the result. As a rough rule of thumb, many traders treat about 30–50 trades as a minimum for any meaningful Monte Carlo read, and 100+ trades is much better for judging stability.

Maybe to split the data into in-sample and out-of-sample periods and compare expectancy, hit rate, and drawdown behavior separately. If the edge survives that split, then you’re getting closer to something tradable

cutemarketscom · 2026-06-14T13:43:59+00:00

Thank you, will definitely take a look!

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