The SignalRoom Manual
Why we are invite-only, how to write powerful research, and how to succeed here.
Why Invite-Only?
Most investment platforms are noisy. They are flooded with low-effort posts, AI-generated spam, and pump-and-dump schemes. SignalRoom is strictly designed to be the opposite.
Writing is a privilege, not a right. We deliberately slow down the investment process to focus purely on quality, manual analysis, and validation. No instant hot-takes. No AI-generated noise. Just deep, proven research.
Writers Invite Writers
Existing high-quality writers vouch for new ones. This creates a chain of trust.
Founder's Circle
Founding members are hand-picked for their proven track record in analysis.
Mastering the Editor
Our editor isn't just a text box. It's a financial canvas. Type / to open the command menu.
Live Tickers
Type /ticker to insert a live price card. Fetches real-time data for stocks, crypto, & ETFs.
Dynamic Charts
Type /chart to drop in an interactive price chart directly into your thesis.
Math Equations
Use /math or KaTeX syntax ($$) for complex valuation models like DCF or Black-Scholes.
Excel Embeds
Upload .xlsx models via /excel. We render a preview table so readers can verify your assumptions.
Bull & Bear Cases
Call out specific risks or catalysts with dedicated /bull and /bear blocks.
PDF & News
Embed earnings reports via /pdf or news links via /news for primary source citations.
Code & Data Power
For deep quantitative analysis, you can execute Python directly in the browser via the /code command. We pre-load pandas, numpy, and our custom sr package.
The `sr` Package
Use the built-in sr helper to fetch institutional-grade data instantly.
# Get real-time price
price = await sr.get_price("AAPL")
# Get historical data (returns DataFrame)
df = await sr.get_history("BTC-USD", period="1y")
# Plot simple Moving Average
df['Close'].rolling(30).mean().plot()Bring Your Own Data
Have a proprietary dataset? Drag and drop any .csv file into the code block to mount it securely in the browser's memory.
import pandas as pd
# Files are mounted to the root directory
df = pd.read_csv("my_portfolio_export.csv")
print(df.describe())Frequently Asked Questions
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