QUANTSUMORE / FINANCIAL DATA & ANALYSIS

Financial data.
Analysis you can verify.

I built and published quantsumore. The archived Python package remains publicly available on PyPI.

Install quantsumore from PyPIpython -m pip install quantsumore==3.0.0b2Archived release · This demo includes local compatibility fixes.

WHY IT MATTERS IN FINANCE

Bring market data, company financials and analysis into Python finance workflows. This playground showcases one feature: nine technical indicators with inspectable calculations.

Financial dataCompany analysisTechnical indicators

PYTHON LIBRARY / LIVE CALCULATIONS

This feature demo runs the tAnalysis module from a locally repaired copy of quantsumore. Explore the full library ↗ · See the calculation checks ↓

QUANTSUMORE / TECHNICAL ANALYSIS PLAYGROUNDPYTHON API · CONNECTING

01 / YOUR INPUT

Type a ticker, upload a CSV or load the sample to run the indicators automatically. Changes to your data, security label, indicator or period update the results.

PRICE INPUT

SECURITY / SYMBOL

Pause after typing a stock or ETF ticker, such as AAPL or SPY, to load up to one year of completed daily prices and run the selected indicator. Source dates and currency appear with the result.

TRY A TICKER · RUNS YOUR SELECTED INDICATOR

INDICATOR

PERIOD / OBSERVATIONS

THE PYTHON CALL

from quantsumore.api import equity
from quantsumore.analysis import tAnalysis

data = equity.Historical(ticker="AAPL",
                         start=start_date, end=end_date)
analysis = tAnalysis(data)
analysis.MovingAveragesAndBollingerBands(sma_period=14, ema_period=14, bb_period=14)

FROM INPUT TO CALCULATION

The Python engine validates the price series, calculates the selected indicator and preserves its warm-up gaps. Inspect the returned values to follow the result.

02 / THE RESULT AWAITING PYTHON

Connecting to the local Python package…

INSIDE THE INDICATOR

Moving averages + Bollinger bands

The math: A rolling mean estimates trend. An exponential average gives recent closes more weight, while Bollinger bands sit two population standard deviations above and below the mean.
The Python: pandas rolling windows compute the mean and standard deviation with ddof=0. A shared, SMA-seeded recurrence updates the EMA and preserves its warm-up gaps.

SMA = mean(close, N); bands = SMA ± 2σ; EMA weight = 2 / (N + 1)

N is the selected window in observations.

ACTUAL PYTHON PACKAGE · QUANTSUMORE 3.0.0b2 · LOCAL COMPATIBILITY FIXESEXPLORE THE SOURCE ↗

CALCULATION CHECKS / 27 OCT 2026

Built to be checked.

Rolling statistics, Wilder smoothing, momentum and volume calculations run in the Python package. All nine indicators passed 661 independent reference comparisons, including their warm-up gaps. Additional tests cover hand-calculated examples, tied prices, zero volume and insufficient history.

Explore the library source ↗

Nine technical indicators: ACTUAL PYTHON PACKAGE
661 reference comparisons: ALL PASSED
Warm-up + edge cases: CHECKED
Security + price-series inputs: VALIDATED
Returned values + CSV export: INSPECTABLE RESULTS

← Back to the Systems Lab Try numbr ↗

Financial data.Analysis you can verify.

Built to be checked.

Financial data.
Analysis you can verify.