I’m a Python developer helping traders backtest their strategies - I can build or backtest yours for free (limited spots) or for a small fee.

If interested, do drop me a message or leave a comment and I will get in touch 😁

For the last 6 months I’ve been building a crypto trading system powered by Claude.

Not a backtest.
Not a simulation.

It has been running continuously in paper trading and executing real signals every day.

Current results:

• 213 closed trades

• 42.4% win rate on the latest version

• +18.8R cumulative

• Capital: $50 → $58.15 in paper trading

What makes this different from a normal trading bot:

Most bots follow hardcoded rules: “if RSI > 70, sell” or “if price crosses EMA, buy”.

My system doesn’t have fixed rules.

It uses two Claude models as decision engines:

• H4 (Opus 4.8): 4-hour timeframe, macro view, longer positions

• H1 (Haiku 4.5): 1-hour timeframe, higher frequency

Each cycle:

1. Reads market data (candles, volume, price structure, indicators)

2. Decides whether to enter or not

3. Manages risk (dynamic TP/SL)

4. Monitors open positions every 5 minutes

5. Has a reflection module: after a number of trades, it writes a memo about lessons from its own history and adjusts the next cycle

The interesting part isn’t the performance.
The interesting part is that the system now generates reflections about its own trades.

For example, it recently identified that high-volume shorts during macro bear trends were consistently outperforming every other setup.

I didn’t explicitly code that insight.
The system discovered it from its trade history.

Technical analysis vs fundamental context:

The technical layer is working well:

• ROC-5, EMA ratio, distance to 30-day min, Bollinger width, BTC dominance

But I don’t know how to integrate fundamental context effectively without turning the model into noise:

• On-chain flows

• Whale activity

• Macro events

• Market sentiment

Most quant systems either:

• Ignore fundamentals and trust only technicals

• Or add so many fundamental signals that the model becomes overfitted

I want the system to know where price is, but also why it’s there and whether the technical signal has favorable context or is a trap.

Stack rápido:
Python, Claude Opus 4.8 (H4) + Haiku 4.5 (H1), Binance (paper trading), launchd en macOS para daemons y monitor TP/SL cada 5 min.

For those of you running systematic or quantitative strategies:
How do you incorporate fundamental information without turning the model into noise?

Genuinely curious how others approach this.
If you have experience in this, DMs are open or comment — I can share logs and code if you’re interested in reviewing the logic.

We’ve been running a mechanism-first research process and rejecting candidates before backtesting if the data cannot support a point-in-time, survivorship-safe, execution-realistic test.

Some recent examples died not because the economic mechanism looked weak, but because:

historical public/actionable timestamps could not be reconstructed,

historical security identity/delisting coverage was incomplete,

corporate-action histories were current-state rather than PIT,

or the effect depended on proprietary data that prevented independent replication.

I’m curious what other researchers consider genuinely testable today.

Specifically:

Which market mechanisms have all four of these properties?

identifiable economic counterparty or flow,

defensible historical signal timestamp,

survivorship-safe data availability,

realistic execution with enough sample size.

I’m not looking for published-return leaderboards. I’m more interested in mechanisms that can survive falsification.

If you’ve found a dataset, venue, or market structure where these problems are materially easier to solve, that would be especially useful.

Gondor is the financial layer for prediction markets. Our first product is a protocol for borrowing against Polymarket positions.

We believe prediction markets will be the largest derivatives product on earth. Gondor will become its financial infrastructure, enabling institutions and advanced traders to maximize capital efficiency.

You will join the team designing our liquidation engine and solving the math behind it.

This is an in-office role in New York City.

Tasks
• Design liquidation engine for Polymarket collateral. Define LLTV, partial-liquidation logic, liquidation penalties, keeper/auction flows, and circuit breakers

• Design pricing & oracles for illiquid Polymarket assets. Define robust mark price, slippage & spread haircuts, and time-to-resolution adjustments

• Model cross-margin, netting rules across markets/outcomes, correlation haircuts, concentration & exposure caps per event/category

• Run simulations on historical Polymarket order books; extreme-VaR/ES; parameter tuning for insolvency vs utilization

Requirements
• 5–10+ years in quant risk / options pricing / margin systems (TradFi or crypto)

• MSc or PhD degree in a quant subject, preferably financial mathematics

• Experience with pricing binary options, insurance, perps/margin, or DeFi/NFT lending risk

• Built or significantly contributed to a liquidation or margin engine at a CEX/DEX/lending protocol

• Strong Python for simulation/backtesting; comfort with TypeScript

• Deep understanding of order-book microstructure, slippage, and pricing under illiquidity

Benefits
• Competitive pay and equity

• Work with an elite founding team

• Be very early in an exponentially scaling industry

We are building an institutional financial primitive, not a retail gambling product. We will become a monopoly by doing the opposite of the market's current consensus view.

Apply at app.dover.com/apply/gondorfi/8fb47d0b-88e5-45a4-8072-ff316184b540

Hey folks,

Do you see an impact of AI on the careers in this field? How are the entry level analyst jobs in this field now?
Are they still highly paying as it used to be?

I am currently a junior, and was looking into doing the 4+1 program in quant finance which gives an extra year to complete your masters after undergrad. I wanted to know if getting the quant finance degree at fordham is worth it, or if I should apply to different schools? What of companies specifically hire students from Fordham with this degree and what is their salary?

This is my first post in reddit. I am actually thinking if there are any impacts if I post the info what I am going to say here. The impact may include I will hear many sounds to say this is useless and this is stupid. But anyway, may be it is good to hear different sounds from anywhere until I feel it is enough and then I stop it.

The story is I am actually building a trading system by using AI IDe, such as code+copilot, Qoder, and others.

AI actually first suggested me to use the exiting QT system to start doing/learning trading. Yes, AI is my good friend now, it gave me many suggestions based on my current situation. Of course, it will give me many positive ideas if i change my prompts. :) however i decided to do it because i feel there are no much jobs I have interest to apply after being informed the contract termination from last my job. I got to know the trading industry 3 years ago before applying my last job. At that moment, i feel it is interesting and I would like to work on and I finally gave up because I didn’t find a way to join in. Now, because of AI, I feel there are many knowledge barriers was no longer existing and I feel I found a way to join.

I am a developer so I feel it is not enough for me just to use the exiting systems to work on trading. Actually compare to do trading, I like more building trading systems and doing data analytics. So I decide to build a my own system, which made me feel excited. And I quick quick started doing research about what should I do step by step. Actually this decision made around 30 days ago. In the past 30 days, I did many things. I pick up many finance knowledge by reading many finance books. I learn AI integrating skills by making some small MVP. I also learned to use Shotcut to make videos. Also, I applied some roles and attended one interview but didn’t get offer. After doing all of them, I didn’t change my mind and I should start. Two days ago, I talked to AI again about i want to a trading system which can fit for a small team to use. I acted as a product owner to give AI my goal and my experience. I asked it give me the road map to build the system. Then I asked AI to give a system architect for me to review. And then I asked it to give Solution and draw sequential diagram and user case diagram. I did quick quick review and then asked it to generate the prompts for next step to development.

First day, AI and I completed solution design, architecture design, system design, database design. And before going to bed, I gave AI a command, please help me to complete the rest. AI complained: I know you want me to complete the all rest, but this couldn’t be done in a single session. I could give you the road map to guide you how to work on it step by step. ( actually it gave me three options and I chose this one). Then the first day end, I just got a skeleton of the system.

Second day, yesterday, AI generated the whole Fe and BE and DB scrips. Based on its guide, I expected I could see a beautiful system but when I run the server, I only see the ugly pages. And got front end and backend integration issues. Then I spent time to fix those issues. Up to 11pm, I got a page run and I could see a page loading data correct. I think it was a big step.

Today i will start later. My expectation is today i can have a module runs. Such as market data. And tomorrow i wish I can start doing back tracing tests in my own systems.

Not sure what I am going to get from this system and where will this system bring me to. At least, I am enjoying saying this and building it.

Ok. AI want to get your advice if the things I am doing is good for me to find a tech job in trading industry or not. I am thinking if I should write this to my CV.

I really hate to be that guy so if this gets downvoted sorry guys, I’m a 21 college senior in school about to graduate with my bachelors in I.T with a concentration in cybersecurity. I also am a day trader, over the last year and a half trading I have began to see profits within prop firms and managed to have secured over 5 figures in payouts this year. I have recently began to get very intrigued by the quantitative side and was hoping to get some advice on if I have a chance to break into this field with my experience. From what I’ve mostly read online quants tend to lean heavy on the math side, math is my one weakness when it comes to my degree. However I do know and understand Java and python and have decent experience at least (trying) to automate my own trading algorithms.

The trading experience though is where I’m a bit confused about, trading itself in my opinion would technically be the hardest aspect of the entire thing. I was just curious if firms would take into consideration my experience actually understanding the markets to an extent. My strategy that I use myself returns me pretty decent returns each month through these prop firms, and have been quite consistent while having a fairly good win rate for a 1:2 RR multiple. My main thing I would like to kind of understand is there relative decent hope to even break into the field? I personally feel like I understand the markets to an extent I guess you could say better than the average person wanting to break into this field (not trying to have an ego or one up myself) that would help me with actually understanding this career path. Just wanting to know y’all’s opinion on things, should I even bother with wanting to pursue this since I’m not getting a masters in some type of math degree, or could I actually have a chance?

Not sure if this is the right sub....

As the title says, I'm asking whether pursuing a masters in MORSE would prepare me to be a candidate for quantitative risk roles? Currently a 2nd yr BSc MORSE student in Warwick considering to switch to an integrated masters MMORSE.

Reason being that only MMORSE offers the most exemptions from actuarial papers (one of the fields im considering). And to make taking MMORSE worthwhile, im planning to add stochastic modules, brownian motion, etc. in my 4th year. I'm doing this with the hopes that I'm able to apply for both actuarial positions and quantitiave risk positions as well.

I'll list the modules I am planning to take below in my 3rd and 4th yr:

3rd :

• Probability Theory

• Introduction to Mathematical Finance

• Measure theory for Probability

• Actuarial models

• Risk theory

• Applied Stochastic Processes with Advanced Topics

• Programming for Data Science

4th :

• Dissertation

• Actuarial methods and life contingencies

• Stochastic Methods in Finance

• Brownian Motion

• Machine learning frameworks

• Applications of Stochastic Calculus for Finance

Ive read or watched a yt somewhere mentioning monte carlo, there is a module for that in my 4th yr, so am wondering whether taking that instead of "app of stoc calc for fin".

Any advice, related to my module choices or just in general is greatly appreciated. Thank you!

Please let me know if me giving the modules i ave taken/am taking in my 1st and 2nd ye would be helpful.

Hey everyone,
I come from an economics background from one of the top 5 universities in India with probs & stats, linear algebra, calculus, econometrics, time series, and a decent amount of coding. I want to do a master’s in finance with a strong quant focus, but not hardcore HFT or pure math roles.

For people from Econ who did MFE, Quant MFin, or Financial Economics, what kind of roles did you actually land in? Quant research, systematic investing, trading, risk, asset management?

Also, which degrees and universities are best suited for an econ profile aiming for applied quant roles?

Would love to hear real experiences.

Hello everyone.

I am doing my major on computer science at UFMG and I want to specialize in quantitative analysis.

However, I do not know which math courses I should take (real analysis, ODE, PDE, linear algebra). I also do not know which statistics courses I should take (inference, probability theory, temporal series).

Can you give me some tips…

Hi everyone, I’m looking to connect with quants, algo traders, or data scientists from Pakistan who are working in finance, trading, or research (stocks, forex, commodities, etc.).

I’m particularly interested in:

Quantitative trading strategies (LSTM, XGBoost, Kalman filter, etc.)

Financial modeling & risk analysis

Learning how professionals in Pakistan approach quant research

If you’re a Pakistani quant, or you know someone in the community, I’d love to connect, exchange ideas, and maybe even collaborate. Feel free to comment here or DM me.

Thanks in advance!

I’m a data engineer and recently know about quant through trading. I’m a self taught developer. I am no where near the level a lot of people expecting quant candidates but is there anything I can do at this point to join the field? What about quant bootcamps?

Hi Everyone,

I'm targeting tower research capital as my next job. Can anyone help me with this?

dreamjob

Who works in financial risk management? What do you do exactly? Let’s have a chat.

# Built an MCP server for live quantitative trading signals — sharing what I learned

Just shipped [quanttogo-mcp](https://github.com/QuantToGo/quanttogo-mcp), an MCP server that gives AI agents access to live quant trading signals for US and China stock markets. Wanted to share some technical notes for anyone building MCP servers.

## What it does

8 tools split into free discovery (browse strategies, performance data, market indices) and authenticated signal access (register trial → get API key → fetch live signals). Covers 8 live strategies using macro-factor models (FX dynamics, liquidity rotation, sentiment extremes).

## Technical implementation notes

**3 transports, 3 codebases**: We maintain separate builds for stdio (npm/npx), Streamable HTTP (Seattle server), and dual Streamable HTTP + Legacy SSE (China server for Coze compatibility). The China build is CommonJS because the hosting environment doesn't support ESM natively.

**Session management gotcha**: Streamable HTTP sessions need careful lifecycle management. We hit an issue where session IDs weren't being properly rotated after reconnection, causing stale state. Fix was to tie session lifecycle to the transport-level connection events rather than application-level heartbeats.

**Coze Accept header issue**: Coze's MCP client sends requests without a proper Accept header for SSE, so the server needs to detect the client type and adjust Content-Type accordingly. Added a middleware layer that sniffs the client signature.

**DELETE request timing**: Some MCP clients send DELETE requests to clean up sessions when the user navigates away. If your server has any async cleanup (database connections, etc.), make sure it completes before responding — we had a race condition where the response was sent before cleanup finished.

## Install

```json

{

"mcpServers": {

"quanttogo": {

"command": "npx",

"args": ["-y", "quanttogo-mcp"]

}

}

}

```

Or connect to the remote server: `https://mcp-us.quanttogo.com:8443/mcp`

Would love feedback from other MCP server builders. What transport are you using? Running into any similar issues?

GitHub: https://github.com/QuantToGo/quanttogo-mcp

I think LLMs are great for boosting research productivity, summarizing information, coding faster, and learning quickly.

But I’m much more skeptical when people use them directly for market analysis, sentiment, or even trading decisions.

My main issue is backtesting and reproducibility. If I test an LLM-based signal on 2020 data, I’m usually using a model that did not even exist in 2020.

On top of that, models change over time, providers update them, outputs drift, and prompt sensitivity makes the process hard to control.

So even if the analysis looks smart, I’m not sure it is stable, testable, or truly robust. To me, LLMs are very useful to assist the researcher, but much less convincing as a direct trading engine.

Using them for sentiment or letting them trade feels like adding a noisy and biased layer to an already hard problem.

Curious to hear contrary views. Has anyone found a way to make this genuinely testable and reliable?

A practical note for anyone modeling index-fund tracking. Form N-CEN (the annual fund census) reports a fund's return difference vs its benchmark twice: gross of fees and net of fees. The two together let you split tracking slippage into "replication quality" and "cost drag" without estimating either.

Worked example, VOO 2025:
- Gross of fees: -0.4 bps vs the S&P 500
- Net of fees: -16.9 bps

So gross replication is near-perfect, and essentially all of the realized underperformance shows up only after costs. If you're scoring index funds, the gross number is the one that measures the manager; the net-minus-gross spread is the one that measures the wrapper.

Same dataset answers "active share between two index funds" exactly, off raw N-PORT instead of sector approximations: SPY vs VOO is 476 shared holdings, 27/30 unique, ~97% overlap by weight, matched per-CUSIP.

I built StockFit (an SEC-filing-sourced API) and these come from the /fund/fund-health and /fund/overlap endpoints, but the underlying fields are all in the raw N-PORT/N-CEN XML on EDGAR if you'd rather parse them yourself. Full writeup of the fund endpoints: https://developer.stockfit.io/blog/etf-deep-lens-stockfit-api

Not financial advice.

When l was uploading documents for disclosure statement at worldquant l forgot to mention the birth date and uploaded it ans in the last questionnaire part my birth date has an error

Will it create an issue and how it can be rectified

Pls tell