SamQuant
Last close118.16Synthetic demo

Historical simulation with visible assumptions

Test the strategy.
Not your luck.

Follow one market line from raw prices to measured risk.

Open research terminalRead research paper

SamQuantResearch

Read the complete research paper.

Methods, tests, assumptions, and limitations.Open research paper

Market data

Clean data before asking it questions.

Each daily bar passes the same checks before strategy code sees it.

InputAAPL.daily.csv
Output262 valid bars
Passed
DateOpenHighLowCloseVolume
2023-04-13100.49101.55100.01100.801,936,241
2023-04-14100.17100.6199.71100.292,851,515
2023-04-17101.84101.98101.01101.672,880,517
2023-04-18102.18103.04101.37102.331,950,815
2023-04-19102.14103.08101.40102.311,620,143
OHLCVPresentMissing0Duplicates0DatesAscendingCacheDeterministic

Strategy

Indicators analyze. Strategies decide.

Two price averages cross. The target position changes.

Output: target weights. No cash changes here.

AAPL / close2023-01-03 to 2024-01-03
PriceLong averageShort average
Target changes5
Reads completed barsReturns target weightsNever executes orders

Execution engine

The strategy decides.
The engine executes.

Every target waits for the next bar before it can become a trade.

SignalBUY AAPL
ValidateCash and position
Order945.855 shares
FillNext open + $100 fee
PortfolioLedger updated
Cash$0+AAPL shares815.455=Final value$96,352
Reject unaffordable buysReject oversized sells

Analytics

Return without risk is half a result.

The portfolio record becomes equity, benchmark, and drawdown curves.

Final portfolio$96,3525 trades
PortfolioBenchmarkDrawdown
Total return-3.6%
Annualized-3.5%
Volatility13.7%
Sharpe-0.19
Max drawdown12.2%
Win rate50.0%

This deterministic result demonstrates the software. It does not claim profitability.

Research terminal

Run the complete system.

Choose the market, dates, strategy, costs, and starting capital.

Open research terminal
RESEARCH / AAPLRUN COMPLETE
Final value $96,352Trades 5Drawdown 12.2%

Methodology

The assumptions stay beside the result.

Signal timing Close today, next open tomorrowTrading costs Fees and adverse slippageBias control Delayed signals and causality testsPublic data Deterministic synthetic OHLCVCurrent version SamQuant 1.1.0Inspect the methodRead the research paper

SamQuant is an educational research tool. Backtested results are hypothetical, depend on historical data and stated assumptions, and do not represent actual trading or guarantee future results. Nothing presented constitutes investment advice.

Architecture

Built to be inspected.

Python owns the financial logic. Interfaces render typed results.

Market dataStrategiesTrading engineAnalyticsInterfaces
ArchitectureDocumentationChangelogResearch paperGitHub repository