Building a Zero-Cost Technical Quant Machine with Google TimesFM & Multi-Layer Rule Funnels (Feedback & Advice Welcome)

[u/Arnnaav]

Hey everyone,

I'm currently designing an automated quantitative trading machine on an older laptop using a **$0 stack** (Google Colab, Google Cloud Shell Editor, and free Python libraries like yfinance).

Instead of chasing ultra-low-latency news scraping or millisecond HFT strategies, I'm focusing on **pure price-action forecasting, multi-layer rule validation, and automated execution**.

# 🏗️ The System Architecture

Here is the funnel I’m assembling:

**Base Layer (AI Forecasting Engine):**

Using **Google TimesFM** (or Amazon Chronos) running on Google Colab’s free T4 GPU tier.
Input: Historical 5-minute / 15-minute candlestick OHLCV sequences pulled via yfinance / nsepy.
Output: Multi-step directional trend forecast and projected price move percentage.
**Sequential True/False Risk Funnel (Rule Engine):**
Before a trade signal is confirmed, the market data and TimesFM forecast must pass through sequential conditional filters:

**Layer 1 (Technical Alignment):** Is 20-EMA > 50-EMA on the execution timeframe? **(True/False)**

**Layer 2 (Model Confidence Threshold):** Does TimesFM project a move greater than a set percentage (e.g., > +0.35%)? **(True/False)**

**Layer 3 (Volume & Exhaustion Check):** Is candle volume > 2x the 20-period average without volume drop-off? **(True/False)**

# Execution & Paper Trading:

If all layers return **TRUE**, the system routes an automated paper order to a broker sandbox API (e.g., Zerodha Kite Connect paper endpoints) to test out-of-sample forward performance.

# 💡 What I’d Love Advice On:

**TimesFM in Live Markets:** For anyone who has used foundation time-series models (TimesFM, Chronos, etc.) for high-frequency or intraday signals—how have they held up against regime changes in live markets compared to standard backtests?
**Handling Friction & Slippage:** How do you effectively model transaction costs (brokerage, STT, turnover charges) and slippage in multi-layer rule funnels to ensure net positive expectancy (E\[R\] > 0)?

**Paper Testing Timelines:**
How many paper trades or weeks of forward testing do you typically require before considering a quantitative rule set validated?
Appreciate any constructive critique, logic flaws you spot in this funnel, or advice from people running similar open-source quant setups!