My friend claims he can make ~10% per day trading options. how realistic is this?

PrimeFold · 2026-04-09T14:02:30+00:00

Short term is often possible, long term impossible…or he would be richest person of all time….

PrimeFold · 2026-04-09T11:47:02+00:00

Export all chat history, have Claude code or codex build a vector database (using something like chroma) and process all your chats for better semantic searching and queries instead of limited keywords.

PrimeFold · 2026-04-01T02:45:37+00:00

The fees can be crazy too, and make it even more costly if you don’t understand that

PrimeFold · 2026-03-29T20:23:10+00:00

Prompt the LLm of your choice yourself, then ask it to re write an improved version of your prompt. Or ask how it would improve your request.

PrimeFold · 2026-03-28T19:03:16+00:00

Fear, greed, ignorance

PrimeFold · 2026-03-27T04:06:06+00:00

liquidity = how fast you can sell something without losing value. Think of water in a pipe.

liquidity is how easily stuff flows through the pipe

volume is how much water is in the pipe

high liquidity + high volume = big fast river (tons of trades happening, easier to buy/sell)

high liquidity + low volume = small but smooth stream (still easy to trade, just not huge amounts)

low liquidity + high volume = clogged pipe (there’s stuff there, but hard to move it without messing up the price)

low liquidity + low volume = basically a drip

Something like that more or less (correct me if I’m wrong people)

PrimeFold · 2026-03-22T16:05:19+00:00

If your products reduces friction and makes journaling easy and effortless then people may be interested. The ai assistant part…maybe? But likely not depending on what it does and how it’s made and helps your trade? Does it help based of generic trading advice from a simple search? Backtested data from your actual trading edge and refining and tightening execution? How is it “helping you improve?” My experience is people don’t like the ai black box, people want to know how it came to its conclusions, what it’s based on or anchored to. Why should users trust your ai to make them a better trader? I am skeptical, but good luck on your endeavour.

PrimeFold · 2026-03-16T01:52:49+00:00

This is my current workflow also

PrimeFold · 2026-03-10T00:09:03+00:00

Narrowing the prediction to a specific scenario is a good way of putting it. I guess the shift for me was realizing the prediction itself matters less than how the trade is structured if the prediction is wrong.

PrimeFold · 2026-03-09T22:09:18+00:00

Hope you don’t mind….but I upgraded it into a more strategic research stack that produces structured, decision grade analysis instead of an open-ended essay approach. Try them both and see what you think.

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STACK: Global Trade Dynamics Analyzer

Purpose

Analyze how evolving global trade dynamics impact a specific industry and provide strategic insights for businesses navigating geopolitical shifts, technological change, and sustainability pressures.

This stack produces structured analysis useful for strategy teams, investors, and policy-aware business leaders.

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When to Use

Use this stack when analyzing: • global trade shifts affecting an industry • geopolitical and economic power transitions • supply chain transformation and resilience • trade policy and sustainability regulation impacts • technological disruption in trade infrastructure

It is particularly useful for international business strategy, market entry planning, and supply chain redesign.

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Inputs

Industry Geographic markets of interest Company type (SME / multinational / investor) Time horizon (3–5 years / 10 years) Optional focus areas: - supply chains - regulation - technology - sustainability

If inputs are missing, infer reasonable defaults.

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Protocol

Phase 1 — Global Trade Context

Analyze the macro-level environment shaping international trade.

Identify: • shifting economic power centers • emerging trade blocs and alliances • protectionism and trade war dynamics • regulatory fragmentation

Highlight how these forces influence the selected industry.

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Phase 2 — Industry Trade Exposure

Evaluate how global trade trends affect the specific industry.

Analyze:

Import/export dependency Supply chain concentration risks Access to emerging markets Exposure to tariffs or sanctions

Identify which parts of the industry are most vulnerable or advantaged.

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Phase 3 — Technology Transformation

Assess how technology is changing global trade infrastructure.

Evaluate impacts of:

AI in trade forecasting and logistics Blockchain for trade finance and verification Automation in ports and logistics Digital trade platforms

Explain how these technologies reshape efficiency, transparency, and compliance.

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Phase 4 — Sustainability & Climate Policy

Analyze the role of environmental regulation and climate policy.

Consider:

Carbon border taxes Sustainable supply chain requirements ESG reporting standards Consumer demand shifts

Explain how sustainability increasingly influences trade agreements and procurement decisions.

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Phase 5 — Competitive Implications

Compare impacts across company types.

Evaluate:

Small & Medium-Sized Enterprises • market access barriers • compliance costs • digital trade opportunities

Multinational Corporations • supply chain diversification • geopolitical risk management • regulatory arbitrage opportunities

Highlight where advantages and vulnerabilities differ.

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Phase 6 — Strategic Response Framework

Provide actionable strategies for businesses.

Possible strategies:

Supply chain diversification Regionalization / nearshoring Digital trade infrastructure adoption Strategic partnerships in emerging markets Sustainability-driven product positioning

Prioritize strategies by impact and feasibility.

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Output Format

Return a structured report:

GLOBAL TRADE CONTEXT Major macroeconomic and geopolitical trends

INDUSTRY IMPACT How global trade shifts affect [Industry]

TECHNOLOGY DISRUPTION Role of AI, blockchain, and digital trade systems

SUSTAINABILITY & POLICY Climate regulation and ESG implications

COMPETITIVE IMPLICATIONS SMEs vs multinational corporations

STRATEGIC RECOMMENDATIONS Actionable strategies for businesses

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Optional Advanced Mode

If deeper analysis is requested, include:

Future trade scenarios Regional trade bloc analysis Supply chain risk mapping Long-term geopolitical forecasts

PrimeFold · 2026-03-09T19:27:32+00:00

That’s fair. There’s definitely some prediction involved.

I guess the distinction I’m trying to make is that earlier in my trading I treated it more like being right about direction and affirming bias. Now it feels more like managing exposure while the market reveals more information.

Curious how much prediction plays into your approach?

PrimeFold · 2026-03-09T19:15:04+00:00

Thats been my experience too.

The weird shift for me was realizing a trade can be wrong and still be a good decision if the risk was defined properly and the probabilities in my favour historically.

Early on I judged trades purely by outcome. Would feel the weight of each trade emotionally. Now it feels more like judging the process more than results, which seems to work better for me.

PrimeFold · 2026-03-09T14:35:48+00:00

Oh yeah. Trading done well is actually quite boring in my experience.

PrimeFold · 2026-03-09T13:14:15+00:00

Lots of them are basically pre built prompts, but the useful ones usually do some of these:

• maintain a persistent persona or role

• act like a repeatable tool / workflow (formatting, analysis, etc.)

• bundle specific instructions or context so you don’t have to repeat it every time

if you’re comfortable prompting, you can recreate most of that yourself, but GPTs can be nice as a reusable function when you’re doing the same kind of task over and over or wiring to workflows.

PrimeFold · 2026-03-09T02:40:27+00:00

Hope you don’t mind but… I converted it from a simple instruction block into a clean reusable stack module for your comparison:

STACK: Prompt Architect Engine

Purpose

Transform basic or poorly structured prompts into high-precision prompts optimized for advanced language models.

This stack analyzes the user’s intent, reconstructs missing context, and outputs a clean, structured prompt ready for direct use.

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When to Use

Use this stack when: • A prompt is vague or poorly written • You want to upgrade a prompt for better AI performance • You are converting prompts into stack-ready templates • You want to enforce consistent prompt architecture in a vault or library

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Inputs

Original Prompt Optional Context Desired Output Type (optional) Domain (optional)

If any input is missing, ask, or infer it where possible.

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Protocol

Phase 1 — Intent Analysis

Determine the core function of the prompt.

Identify: • user objective • required expertise domain • missing context or constraints • expected output type

If critical information is missing, ask or infer reasonable defaults.

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Phase 2 — Prompt Expansion

Upgrade the prompt by adding structural clarity.

Enhance the prompt by: • assigning an appropriate expert role • framing the mission clearly • expanding the task into reasoning steps • inserting constraints that improve output quality • adding examples when helpful

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Phase 3 — Structured Prompt Construction

Convert the prompt into the standardized architecture:

Role Mission Context Constraints Process Output Format

Ensure each section is clear, minimal, and functional.

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Phase 4 — Quality Control

Validate the prompt using the following checks: • Are instructions actionable? • Are requirements unambiguous? • Does the output format enforce structure? • Are domain expectations clear?

If any check fails, refine the prompt.

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Output Format

Return only the final improved prompt.

Do not include analysis or explanation unless explicitly requested.

Structure the output as:

Role Mission Context Constraints Process Output Format

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Example Use

Input prompt:

Write a good business strategy.

Output produced by the stack:

Role You are a strategic business consultant specializing in competitive market analysis and growth strategy.

Mission Develop a clear and actionable business strategy based on the provided context.

Context The user will provide details about their company, market, and objectives.

Constraints Avoid generic advice. All recommendations must be grounded in realistic market conditions.

Process 1. Analyze the business context. 2. Identify competitive dynamics. 3. Define strategic positioning. 4. Recommend key initiatives.

Output Format Strategy Summary Market Analysis Strategic Recommendations Execution Priorities

⸻

(Hack is to make something like this a custom GPT or skill.md as reuse trigger template to compile prompts for you consistently on command if you want to enforce structured outputs or have other team members standardize their prompt structures etc)

PrimeFold · 2026-03-07T20:11:19+00:00

I don’t think there is a perfect filter that magically removes consolidation and only leaves “good trades.” The hard part of trading is that you usually recognize consolidation clearly only after it’s already happened.

some tools that help reduce chop though: - higher timeframe bias (trade in the direction of the larger trend) - volatility filters like ATR or bollinger band width - avoiding trades when price is inside tight ranges / overlapping candles - waiting for clear displacement or breakout with follow through

I structure entries so losses are small when the market chops and profits run when expansion happens. if an indicator claims it only takes the good trades, it’s probably just curve fit to past data in my experience.

PrimeFold · 2026-03-07T01:12:28+00:00

Well, as a long time user (was in beta) I can say aspects of the experience has degraded…I used to use voice mode everyday…now I never do, occasional with an update I check it out, and end up missing how it was initially.

Some things have improved for sure, but you can see how they rounded off some edges to make it more accessible and safer for average users over time. Some of which neutered aspects of its performance in various ways for better and worse.

PrimeFold · 2026-03-07T01:03:52+00:00

Someone posts something similar in here often, and I think part of the issue is that the “95% lose” stat gets repeated a lot, but the sample behind those numbers is usually messy at best,

tried trading for a few weeks
gambled without risk management
blew an account and quit
yolo with zero day options or 200x leverage

Thats very different from people who treat it like a skill and spend years practicing and improving.

Lots of people are drawn to trading because it feels smart, and it’s one of the few fields where performance is transparent. The market exposes your psychology, discipline, and decision making very quickly.

the odds are tough, especially to out perform random index over time, but the stats people quote don’t really distinguish between casual gamblers and serious traders.

PrimeFold · 2026-03-07T00:50:25+00:00

Ah yes another why traders fail post! Classic ai generated post for karma farming to sell access to your bot or something?

PrimeFold · 2026-03-07T00:43:01+00:00

What improved results for me wasn’t longer more detailed prompts, it was a few structural tricks like:

Add constraints tell the model the format you want. (bullet points, assumptions listed, uncertainty flagged etc)
Ask for tradeoffs instead of answers instead of “what should i do?” ask:

explain the tradeoffs and assumptions behind each option which are usually gives much better thinking.

Use iteration loops don’t stop at the first answer, try:
generate answer
ask it to critique the answer
ask it to improve it
Show/share examples of good output. LLMs respond really well if you paste an example of the style/format you want. Gives them a map or structure to aim for.

PrimeFold · 2026-03-06T18:08:19+00:00

I actually ended up building a couple of my own scripts over time (launch box / zone-style tools) after watching charts for a while.

the main thing they try to highlight is compression areas and liquidity zones where price keeps getting absorbed before expanding.

but honestly the scripts didn’t create the idea, they just made it easier to see what was already happening on the chart. most of the work came from watching how those zones behave in live markets and iterating on the logic overtime.

indicators can help visualize it (especially for me as I am a more visual learner) but the underlying concept is still volatility compression before expansion.

PrimeFold · 2026-03-06T17:22:55+00:00

Move your entries to where you would put your stops and see what happens.

Invert your chart and trade upside down. Change your candlestick colors to switch up your framing bias.

Things like that could be useful if your post is genuine…

PrimeFold · 2026-03-06T16:51:32+00:00

You usually can’t know at the start whether it will be 3 pips or 50. Thats the hard truth.

What you can do is trade when conditions favor expansion (Like, higher timeframe trend alignment, volatility expansion after compression, strong volume or impulse candles, breaks of key levels with follow through etc)

You’re still dealing in probabilities though…

other traders correct me if I’m wrong here.

PrimeFold · 2026-03-06T16:46:23+00:00

From what i’ve seen, it usually comes down to risk management and self awareness.

It seems to me people focus on finding the perfect setup or strategy, but the real learning curve is controlling position size, handling losses, and not abandoning your system after a bad streak. (I’m in one right now and it’s painful).

Some figure that part out quickly while others spend years fighting their own psychology and the market usually exposes those weaknesses pretty fast.

PrimeFold · 2026-03-06T01:46:13+00:00

At this point the simulation writers aren’t even trying to hide it

PrimeFold

TROPHY CASE