Market Signals Toolkit

1.0 Why a finance student codes

The analysts NM wants to work beside don't type numbers into Excel by hand. They pull data, clean it, and let it argue with them. He decided to learn that now rather than wish he had later — on weekends, next to a full finance courseload, with no course credit attached. The toolkit is the receipt.

2.0 The logic, running live

Below is the toolkit's crossover detector on a synthetic price series — generated in this page, so no market data is misrepresented. When the 50-day average crosses above the 200-day, the toolkit flags it: the golden cross. Hover anywhere for the values.

3.0 Under the hood

The core is deliberately compact — every line accountable, nothing copy-pasted from a tutorial he couldn't defend:

# pull → clean → compute → flag
prices = pull(ticker).clean()
sma50, sma200 = prices.rolling(50).mean(), prices.rolling(200).mean()

cross = (sma50.shift(1) <= sma200.shift(1)) & (sma50 > sma200)
if cross.any():
    flag("GOLDEN CROSS ▲", date=cross.idxmax())   # same rule as the chart above

4.0 Roadmap

Next release adds a backtesting module — measuring how the flagged signals would actually have performed, because a signal without a hit-rate is an opinion. That instinct, applied to his own tool, is the point of the whole exercise.

“I built it, I understand every line, and it runs every week — the toolkit does the first read of the market before I've poured the coffee.”