In partnership with
White Castle is Now Expanding at .001% the Cost
No, not with new brick-and-mortar stores. With robotic kiosks from ART.
ART, aka Automated Retail Technologies, makes serving food 24/7 possible for brands like White Castle at .001% the cost of a new brick-and-mortar.
Their robotic kiosks dish out customers favorite dishes, like warm White Castle sliders, on demand in seconds.
That makes entering new markets a three-step process. Plug it in. Stock it. Turn it on. And it isn’t just White Castle benefitting.
Other big-name food brands like Nestlé and Macaroni Grille partnered too, and foodservice behemoths like Sysco and Aramark also use it. Even better, until April 25, you can earn guaranteed bonus stock as an early-stage ART investor and share in their growth.
This is a paid advertisement for Automated Retail Technologies Regulation CF offering. Please read the offering circular at https://invest.automatedrt.com/
② One strategy in this book returned 2.3× the S&P 500 on a risk-adjusted basis over 5 years.
Fully coded in Python. Yours to run today.
The 2026 Playbook — 30+ backtested strategies,
full code included, ready to deploy.
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The AlgoEdge Quant Finance Bootcamp — 12 weeks of stochastic models, Black-Scholes, Heston, volatility surfaces, and exotic options. Built from scratch in Python.
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Premium Members – Your Full Notebook Is Ready
The complete Google Colab notebook from today’s article (with live data, full Hidden Markov Model, interactive charts, statistics, and one-click CSV export) is waiting for you.
Preview of what you’ll get:
Inside the Strategy Lab
Install and imports — installs
eodhdandsmartmoneyconcepts, loads numpy, pandas, matplotlib, plotly, and thesmcclass, with fivethirtyeight plot style applied globallyAAPL.US data fetch — pulls 192 rows of daily OHLCV data for 2025-01-02 to 2025-10-08 via the EODHD API, prints shape, column info, date range, and a descriptive stats table
Interactive OHLC chart — Plotly candlestick + volume subplot with shared x-axis, followed by setting the date column as the DataFrame index to prepare the
ohlcobject for SMC analysisFVG signal computation — applies
smc.fvg(join_consecutive=False), prints bullish and bearish signal counts, and builds the Top/Bottom/FVG signal DataFrameFVG signal visualization — scatter plot overlaying green and bearish red star markers on the close price chart, with evenly spaced date tick labels
FVG backtest — shifts the FVG signal by 1 bar to eliminate lookahead bias, builds the position series, calculates daily and cumulative returns, and plots both vs Buy and Hold with a total return printout
Swing Highs and Lows computation — applies
smc.swing_highs_lows(swing_length=5)for weekly swing detection, prints high and low signal counts, and builds the HighLow/Level signal DataFrameSwing signal visualization and backtest — plots swing highs (green) and lows (red) on price, runs the bias-free backtest (long at swing lows, exit at swing highs), plots daily and cumulative returns vs Buy and Hold, plus a swing length sensitivity analysis looping over lengths 3, 5, 7, 10, 15, and 20 showing total return and the multiplier vs Buy and Hold for each
Three-way comparison — combined cumulative returns chart overlaying all three strategies (Buy and Hold, FVG, Swing H/L), a full performance summary table covering Total Return, Annualized Volatility, Sharpe Ratio, and Max Drawdown, plus a signal count comparison explaining why Swing signals are fewer and more selective
Conclusions and final summary — conclusions table mapping each strategy to its key insight, practical tips for avoiding false SMC signals, and a final printout showing exact return multiples of Swing H/L vs FVG and vs Buy and Hold
Free readers – you already got the full breakdown and visuals in the article. Paid members – you get the actual tool.
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