Vibecode MonkStreet
track this build5 steps, step by step0%KINDA · weekend project
price $200/moyou'd save $2,400/yrbuild time a weekendcategory 💰 personal financereplaced by 0 people
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.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of MonkStreet. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # MonkStreet indie build ## Goal Build the smallest trustworthy replacement for the core MonkStreet workflow for one developer or a tiny team. ## Scope Pulls daily price data for a chosen universe, ranks it with momentum and value style factors, backtests the ranking with walk-forward rebalancing, and emails you the current top and bottom names. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - Point-in-time fundamentals and delisted tickers, so your backtest quietly assumes the losers never existed - Clean handling of splits, dividends, mergers and index reconstitutions - Whatever research process, however good or bad, sits behind the paid signal - Someone else's conviction to blame when a position goes against you - Any institutional data feed: short interest, filings parsing, tick data, borrow costs If those capabilities are essential, use MonkStreet instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief 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. ## Required capabilities - Python 3.11 and a machine that can run a nightly job - a market data source with an API key, free tier is fine for daily bars - an SMTP account or similar for the daily digest - enough statistics to distrust your own backtest ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a lean indie version of MonkStreet. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # MonkStreet indie build ## Goal Build the smallest trustworthy replacement for the core MonkStreet workflow for one developer or a tiny team. ## Scope Pulls daily price data for a chosen universe, ranks it with momentum and value style factors, backtests the ranking with walk-forward rebalancing, and emails you the current top and bottom names. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - Point-in-time fundamentals and delisted tickers, so your backtest quietly assumes the losers never existed - Clean handling of splits, dividends, mergers and index reconstitutions - Whatever research process, however good or bad, sits behind the paid signal - Someone else's conviction to blame when a position goes against you - Any institutional data feed: short interest, filings parsing, tick data, borrow costs If those capabilities are essential, use MonkStreet instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief 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. ## Required capabilities - Python 3.11 and a machine that can run a nightly job - a market data source with an API key, free tier is fine for daily bars - an SMTP account or similar for the daily digest - enough statistics to distrust your own backtest ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a production product version of MonkStreet. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== PRODUCT.md ===== # MonkStreet product brief ## Problem 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. ## Product outcome Pulls daily price data for a chosen universe, ranks it with momentum and value style factors, backtests the ranking with walk-forward rebalancing, and emails you the current top and bottom names. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - Python 3.11 and a machine that can run a nightly job - a market data source with an API key, free tier is fine for daily bars - an SMTP account or similar for the daily digest - enough statistics to distrust your own backtest ## Explicit non-goals for v1 - Point-in-time fundamentals and delisted tickers, so your backtest quietly assumes the losers never existed - Clean handling of splits, dividends, mergers and index reconstitutions - Whatever research process, however good or bad, sits behind the paid signal - Someone else's conviction to blame when a position goes against you - Any institutional data feed: short interest, filings parsing, tick data, borrow costs ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees. ===== ARCHITECTURE.md ===== # Architecture ## Starting brief 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. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it. ===== AGENTS.md ===== # Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone. ===== MILESTONES.md ===== # Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations. ===== OPERATIONS.md ===== # Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted MonkStreet capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
README.md
# MonkStreet indie build ## Goal Build the smallest trustworthy replacement for the core MonkStreet workflow for one developer or a tiny team. ## Scope Pulls daily price data for a chosen universe, ranks it with momentum and value style factors, backtests the ranking with walk-forward rebalancing, and emails you the current top and bottom names. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - Point-in-time fundamentals and delisted tickers, so your backtest quietly assumes the losers never existed - Clean handling of splits, dividends, mergers and index reconstitutions - Whatever research process, however good or bad, sits behind the paid signal - Someone else's conviction to blame when a position goes against you - Any institutional data feed: short interest, filings parsing, tick data, borrow costs If those capabilities are essential, use MonkStreet instead of pretending the gap is solved.
AGENTS.md
# Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs".
BUILD_PLAN.md
# Build plan ## Original build brief 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. ## Required capabilities - Python 3.11 and a machine that can run a nightly job - a market data source with an API key, free tier is fine for daily bars - an SMTP account or similar for the daily digest - enough statistics to distrust your own backtest ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden.
.env.example
# Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
PRODUCT.md
# MonkStreet product brief ## Problem 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. ## Product outcome Pulls daily price data for a chosen universe, ranks it with momentum and value style factors, backtests the ranking with walk-forward rebalancing, and emails you the current top and bottom names. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - Python 3.11 and a machine that can run a nightly job - a market data source with an API key, free tier is fine for daily bars - an SMTP account or similar for the daily digest - enough statistics to distrust your own backtest ## Explicit non-goals for v1 - Point-in-time fundamentals and delisted tickers, so your backtest quietly assumes the losers never existed - Clean handling of splits, dividends, mergers and index reconstitutions - Whatever research process, however good or bad, sits behind the paid signal - Someone else's conviction to blame when a position goes against you - Any institutional data feed: short interest, filings parsing, tick data, borrow costs ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees.
ARCHITECTURE.md
# Architecture ## Starting brief 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. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.
AGENTS.md
# Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone.
MILESTONES.md
# Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.
OPERATIONS.md
# Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted MonkStreet capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
$ choose a build depth, inspect the files, then open the complete pack in your agent · this prompt is generated from the build plan · improve it via PR
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.
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
Nothing worth pointing at. That's why the prompt exists.
share on X ↗"I just replaced MonkStreet ($200/mo) with one prompt"
Vibecode MonkStreet
Kinda. The core of MonkStreet is buildable in a weekend with the prompt on this page, but there are real gaps: Point-in-time fundamentals and delisted tickers, so your backtest quietly assumes the losers never existed, Clean handling of splits, dividends, mergers and index reconstitutions. Read the honest list above before committing.
How much does MonkStreet cost?
MonkStreet costs about $200/month (MonkStreet Annual, checked 2026-08-18), which is $2400 per year.
What do I lose by replacing MonkStreet?
Honestly: Point-in-time fundamentals and delisted tickers, so your backtest quietly assumes the losers never existed; Clean handling of splits, dividends, mergers and index reconstitutions; Whatever research process, however good or bad, sits behind the paid signal; Someone else's conviction to blame when a position goes against you; Any institutional data feed: short interest, filings parsing, tick data, borrow costs. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to MonkStreet?
No mature open-source alternative worth pointing at, which is exactly why the one-shot prompt on this page exists.