¿Puedo vibecodear MonkStreet?

PARCIAL · un fin de semana

precio $200/mesahorrarías $2,400/añotiempo de build un fin de semanacategoría 💰 finanzas personalesreemplazada por 0 personas

The software half of a signals service is genuinely small: pull daily bars, compute factors, rank a universe, backtest with walk-forward windows, email yourself the top names. An agent will get you that in a weekend, and it will look uncomfortably similar to what you are paying for. What you cannot one-shot is point-in-time fundamentals, survivorship-bias-free universes and clean corporate actions, which is exactly where homemade backtests turn into fiction that says 40 percent a year. The unverifiable part is whether the paid edge is real, because no subscriber gets to audit it either. So build the harness, use it to think, and do not confuse a green equity curve on free data with alpha.

el prompt

Build a local quant research harness for a single user. No accounts, no hosting, no telemetry.

Stack: Python 3.11, uv for deps, DuckDB for storage, pandas and numpy for math, Streamlit for the UI, plain SMTP for the daily digest. No cloud services, no Docker, no web framework.

Data: fetch daily adjusted OHLCV for a ticker list in universe.txt from one provider behind a single fetcher module, so it can be swapped. Cache every response into DuckDB and never refetch a date range you already have. Provider key and SMTP creds live in .env, loaded with python-dotenv, and .env is gitignored with a .env.example committed.

Build these pieces:
1. ingest.py: incremental daily bar download into DuckDB, idempotent, logs how many rows were added per ticker.
2. factors.py: compute 12-1 momentum, 60-day volatility, 200-day trend filter, and a simple value proxy from whatever fundamental fields the provider gives for free. Each factor is a pure function of a price frame, cross-sectionally z-scored, missing data handled explicitly rather than dropped silently.
3. backtest.py: monthly rebalance, long the top decile of a weighted factor blend, equal weight, configurable transaction cost in basis points, walk-forward so factor weights are fit only on data before each test window. Output CAGR, max drawdown, Sharpe, turnover, hit rate, and a per-year table. Print a loud warning that the universe is survivorship biased and the fundamentals are not point-in-time.
4. app.py: Streamlit page with the equity curve, the yearly table, the current ranked table, and a slider for factor weights that re-runs the backtest.
5. digest.py: a script for cron that emails today's top 20 and bottom 20 with their factor scores.

Out of scope: intraday data, options, order execution, broker integration, portfolio accounting, tax lots, auth, multi-user anything.

Include pytest tests for the factor functions and for one known-answer backtest on synthetic data. Write a README that states plainly what the data limitations invalidate.

$ ábrelo en tu agente (prompt listo, tú das enter) o cópialo crudo

Se puede en parte

Ahorro de referencia: US$2400/año si dejas de pagar MonkStreet.

Quiero que lo armen →

por qué la gente sigue pagando

People pay for a decision, not for code. A signals subscription outsources the part that is actually hard, which is committing to a rule and sticking with it through a drawdown, and it does so with a narrative confident enough to hold onto. There is also the data gap: a paid service can license clean point-in-time fundamentals that a hobbyist cannot, and that difference shows up as backtests that are less flattering and more honest. The uncomfortable part is that from the outside you cannot tell a licensed, carefully validated process from a spreadsheet with good copywriting, and both charge monthly.

qué pierdes

xPoint-in-time fundamentals and delisted tickers, so your backtest quietly assumes the losers never existed

xClean handling of splits, dividends, mergers and index reconstitutions

xWhatever research process, however good or bad, sits behind the paid signal

xSomeone else's conviction to blame when a position goes against you

xAny institutional data feed: short interest, filings parsing, tick data, borrow costs

arte previo · úsalo en vez de construir, si prefieres

Nada que valga la pena señalar. Por eso existe el prompt.

compartir en X ↗"Acabo de reemplazar MonkStreet ($200/mes) con un prompt"