How to use Stratos
Everything you need to know about building strategies, running backtests, and leveraging Orbit AI to find your edge.
What is Stratos?
Stratos is a quantitative trading research platform that lets you design, rigorously test, and iterate on systematic trading strategies — without writing a single line of Python. Describe a strategy in plain English to Orbit, tweak it in the visual builder, and run it against years of institutional-grade market data in seconds.
Build
Describe your strategy to Orbit or configure it manually using the visual builder. Combine indicators, set risk rules, choose your universe.
Test
ATLAS runs your strategy against historical data from 2015 onward. Monte Carlo simulations separate genuine edge from luck.
Iterate
Use the results dashboard and Orbit's analysis to understand what worked, what didn't, and how to sharpen your edge.
Research only — for now. Stratos is currently a backtesting and research platform. It does not connect to brokerages or execute live trades. Live Alerts, Live Deployment, and Broker Integration are coming soon.
The Strategy Builder
The Strategy Builder at /strategies is where you configure every aspect of your strategy. It has six main areas.
1. Asset Universe
Select one or more tickers to trade. You can also add supplementary tickers (e.g., SPY, QQQ, VXX, TLT) whose data feeds into your entry logic — useful for macro-conditional strategies without trading those assets directly.
Stratos supports a curated universe of US equities, sector ETFs, commodity ETFs, bond ETFs, and leveraged instruments during beta. The asset universe expands post-beta.
2. Timeframe & Period
Choose the bar resolution and the historical window to test over.
Intraday bars use Alpaca minute data. Daily and above use adjusted OHLCV.
Or pick exact start/end dates for a custom date range.
3. Indicators
Enable the technical indicators you want available in your entry and exit logic. Each has configurable parameters — once enabled, their computed values can be referenced directly in conditions.
Relative Strength Index
Exponential Moving Average
Simple Moving Average
MACD + Signal Line
Upper, Middle, Lower Bands
Average True Range
Average Directional Index
%K and %D oscillator
Volume-Weighted Avg Price
Price momentum
Commodity Channel Index
Williams Percent Range
Automatic warmup period
ATLAS automatically determines the longest indicator period you have configured and removes that many bars from the start of the backtest. For example, if you enable SMA(200), the first 200 bars are excluded from signal generation — preventing false signals from incomplete indicator values. Your effective backtest window is slightly shorter than configured when using long-period indicators.
4. Strategy Mode
Choose whether your strategy trades Long, Short, or both directions. This determines how many sides you need to configure — Long and Short have completely independent entry logic and risk management.
Long Only
Only takes buy positions. Standard for equity strategies.
Short Only
Only takes short positions. For bearish or hedging strategies.
Long + Short
Takes both directions. Configure entry logic and risk for each side independently.
5. Entry Logic — Three Modes
Stratos supports three distinct ways to define when to enter a trade. In Long + Short mode, each side gets its own independent entry logic.
Visual condition builder. Add rows of conditions joined by AND / OR logic. Each condition has a left operand, an operator, and a right operand. Supports modifiers like previous bar, rolling average, and percent change.
Quick presets: Momentum Breakout, RSI Oversold, 3% Gap Up, Trend Following.
Write entry logic as a Python-style boolean expression. Reference any enabled indicator by name. Supports all standard operators and parenthetical grouping.
(RSI < 30) & (Close > EMA_50) & (MACD > MACD_signal)Use a trained SmartLab model as your entry signal. Select a model you've trained in SmartLab — it outputs a BUY signal when its prediction meets your threshold. Requires a trained model in your account.
6. Risk Management
Risk rules are enforced at the per-trade level by the ATLAS engine, applied independently per side (Long / Short).
- Percentage — Fixed % below entry price (e.g., 2%)
- ATR-based — Multiple of Average True Range (e.g., 2x ATR)
- Indicator — Exit when price crosses a level (e.g., below EMA_50)
- Trailing Stop — Activates after a gain threshold, then trails by a set %
- Single — Exit 100% of position at a target % gain
- Scaled / Partial — Exit in tranches (e.g., 50% at 5%, rest at 10%)
- Indicator — Exit when price crosses a technical level
Running a Backtest
Once your strategy is configured, click Run Backtest. The ATLAS engine processes your strategy as a background job — you'll see a live status overlay while it runs.
Data Fetch
ATLAS pulls historical OHLCV bars from Alpaca for all selected tickers across your chosen period and timeframe.
Indicator Calculation
All enabled indicators are computed on the full dataset. The warmup period (longest indicator period) is automatically removed from the signal window.
Signal Generation
Your entry logic is evaluated bar-by-bar. Executions fill at the open of the next bar to eliminate look-ahead bias.
Risk Management
Take-profit and stop-loss levels are checked on every subsequent bar. Slippage and commission are modeled in.
Monte Carlo Analysis
The engine runs 1,000+ simulations by resampling trade outcomes to statistically validate your edge.
Results
You're redirected to the results dashboard with full metrics, equity curve, trade log, and Monte Carlo distribution.
No look-ahead bias. All trades fill at Open[T+1] — the opening price of the bar after the signal. Your strategy never uses data it couldn't have known at signal time.
Understanding Your Results
The results page shows a professional-grade quantitative breakdown. Every metric is color-coded using standard financial analysis thresholds — green means good, amber is acceptable, red needs attention.
Core Performance Metrics
Total Return
Cumulative percentage gain or loss over the backtest period.
Sharpe Ratio
Risk-adjusted return: how much excess return you earn per unit of volatility. Higher is better.
Max Drawdown
The largest peak-to-trough decline in portfolio value. Tells you the worst-case capital loss at any point.
Win Rate
Percentage of trades that closed profitable. Pair with Profit Factor for a complete picture.
Profit Factor
Gross profit divided by gross loss. A factor of 2.0 means you made $2 for every $1 lost. Below 1.0 is net-losing.
Equity Curve
Your portfolio value over time, plotted alongside the benchmark (buy-and-hold SPY). The gap between the two lines is your alpha.
Monte Carlo Analysis
ATLAS runs 1,000+ simulations by randomly resampling your trade outcomes. This answers the critical question: is this edge real, or am I just seeing a lucky sequence?
Median Return
The 50th-percentile return across all simulations. Your most realistic expected outcome.
5th Percentile
The worst-case return in 95% of simulations. A conservative lower bound on your downside.
95th Percentile
Best-case return in 95% of simulations. Shows upside potential under favorable trade sequencing.
Risk of Loss
Fraction of simulations that ended with a net loss. Lower is better.
Prob. Beat Benchmark
Percentage of simulations that outperformed buy-and-hold SPY. Above 70% indicates a robust edge.
Return Std Dev
Volatility of simulated returns. Lower means results are more consistent across different trade sequences.
Trade Log
Every individual trade is listed — entry date, exit date, entry price, exit price, return, and exit reason (take-profit hit, stop-loss triggered, or signal exit). Use this to audit the strategy's behavior trade-by-trade.
Tip: Click Edit Strategy on any results page to reload that exact configuration into the Strategy Builder — tweak and re-run instantly.
Orbit AI
Orbit is your quantitative co-pilot. It understands financial markets, knows the ATLAS engine inside out, and can configure your entire strategy from a single sentence.
Strategy Generation
Describe a strategy in plain English. Orbit configures tickers, indicators, entry logic, and risk parameters automatically.
Form Population
After generating a strategy, Orbit directly fills in the Strategy Builder — you just review and run.
Results Analysis
Ask Orbit to explain your backtest results. It reads the actual metrics and gives data-backed explanations.
Strategy Refinement
Tell Orbit what you want to change. It modifies only the relevant fields and explains its reasoning.
Quant Education
Ask anything about indicators, risk metrics, or market concepts. Orbit explains like a senior quant analyst.
Conversation Memory
Orbit remembers context within a session. Iterate naturally without restating the full strategy each time.
Example Prompts
“Build me a mean reversion strategy on QQQ that buys when RSI drops below 30 and sells when MACD crosses above the signal line.”“Create a trend-following strategy on NVDA using EMA crossover — go long when the 20 EMA crosses above the 50 EMA, with a 3% stop loss.”“Why did this strategy underperform during 2022? What market regime caused the drawdown?”“Adjust the take profit to 8% and add a trailing stop that activates after a 5% gain.”“Build a momentum strategy that only enters if SPY is above its 200-day moving average — use SPY as a macro filter.”“What does a Sharpe ratio of 0.7 mean, and how do I improve it?”SmartLab — ML Models
SmartLab is Stratos' machine learning studio. Instead of rules like “buy when RSI < 30”, you train a model to learn entry patterns directly from price data — then use it as the signal source in the Strategy Builder.
Create a Model
Go to SmartLab and click New Model. Give it a name, choose a prediction target (Classification for Up/Down direction, Regression for return magnitude), and select an algorithm core.
Configure & Train
Select the ticker, timeframe, and training window. Choose walk-forward validation (recommended — trains on rolling windows to prevent overfitting) or a fixed train/test split.
Use in a Strategy
In the Strategy Builder, set Entry Mode to ML and select your trained model. ATLAS uses its predictions as the entry signal during backtesting.
Random Forest
Ensemble method, robust to overfitting. Good default choice.
XGBoost
Gradient boosting, high performance on structured data.
Linear / Logistic
Simple, interpretable baseline for directional prediction.
Tips & Best Practices
Start with Orbit, refine in the Builder
Let Orbit generate the initial strategy from a description, then open the Builder to manually fine-tune indicators, periods, and risk levels. This is the fastest iteration loop.
Test longer periods first
A 5–10 year backtest includes multiple market regimes (bull, bear, choppy). Strategies that only work in one regime aren't robust. Good edge survives 2020, 2022, and 2024.
Trust Monte Carlo over point-in-time returns
A 50% return on a single run could be a lucky sequence. If the Risk of Loss is above 25% or the 5th percentile is deeply negative, the edge is fragile regardless of the headline return.
Use supplementary tickers as macro filters
Add SPY or TLT as a supplementary ticker and reference them in your entry logic (e.g., only go long when Close > SPY_EMA_200). This is a classic regime filter that reduces drawdown in bear markets.
Account for indicator warmup when choosing periods
If you enable SMA(200) and test over 1 year of daily data (~252 bars), ATLAS removes the first 200 bars as warmup — leaving only ~52 bars for signal generation. Use longer test periods when using long-lookback indicators.
Size your stop loss to the timeframe
A 2% stop on a 1-minute chart will get hit by noise. A 1% stop on a daily chart may be too tight. ATR-based stops automatically scale to the asset's current volatility.
Use the Trade Log to audit signal quality
If most losses are stop-loss exits rather than signal exits, your stop is too tight. If you have very few trades over a long period, your entry conditions are too restrictive.
What's Coming Soon
Stratos is actively developed. Here's what's on the near-term roadmap.
Paper Trading Mode
In DevelopmentRun strategies in real-time against live market data without risking capital. Validate that your backtest results hold up in live conditions before committing.
Live Deployment & Broker Integration
In DevelopmentConnect Stratos directly to your brokerage. Execute strategies live with the same logic you validated in backtesting — closing the loop from research to real money.
Expanded Asset Universe
PlannedBroader coverage of US equities, options data, and crypto pairs. The beta uses a curated list — post-beta opens the full market.
Advanced SmartLab Models
PlannedAdditional ML architectures and signal-filtering layers that stack on top of your existing indicator logic for ensemble-style strategies.
Frequently Asked Questions
Where does your market data come from?
We source institutional-grade historical OHLCV data from Alpaca Markets. Minute and intraday data comes from their market data API; daily and weekly bars use adjusted prices to account for splits and dividends.
Does Stratos execute real trades or connect to my brokerage?
Not yet. Stratos is currently a research and backtesting platform. Live Deployment and Broker Integration are in development and coming soon.
How accurate are the backtests?
ATLAS is validated against QuantConnect across multiple strategy types and timeframes — results match within acceptable variance (differences are attributable to intraday bar data provider differences, not engine errors). All fills use Open[T+1] pricing to eliminate look-ahead bias. Slippage and commissions are modeled.
Can Orbit see my strategies or backtest results?
Orbit can only read a strategy or backtest when you explicitly ask it to within a conversation. We do not use your strategy logic or results to train shared models or share them with other users. Your alpha is yours.
What assets can I backtest?
During beta, Stratos supports a curated universe of US-listed stocks (S&P 500 constituents and popular growth stocks), index ETFs (SPY, QQQ, IWM, DIA), sector ETFs, commodity ETFs, bond ETFs, and leveraged ETFs. The full asset universe expands post-beta.
How far back does historical data go?
Daily data is available from approximately 2015 onward for most assets. Intraday minute data availability varies by ticker — most liquid names have 5+ years of intraday history.
Can I backtest multiple tickers at once?
Yes. Add multiple tickers to your asset universe and the ATLAS engine tests each one independently, then aggregates results. You can also add supplementary tickers (like SPY or VXX) as macro data inputs without trading them directly.
What programming knowledge do I need?
None required for the Builder or ML modes. Code mode uses a simple Python-style boolean expression syntax — no functions, imports, or classes. If you can write a spreadsheet formula, you can write entry logic in Code mode.
What is a Founding Member?
Founding Members are early users who locked in a discounted lifetime rate during the beta launch period. They receive the same full platform access as paid subscribers, at a permanently reduced price.
Still need help?
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