Computer Science A
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Code Runner - Java
Code Runner - Examples
Code Runner - JavaScript
FRQ - Methods and Control Structures
Challenge Submission Test
2021 FRQ 3
2023 FRQ 3
2024 FRQ 3
2024 FRQ 2
2024 FRQ 1
2024 FRQ 4
FRQ 2 - Sign Class
2023 FRQ 1
2021 FRQ 2
2019 FRQ 4
2019 FRQ 2
2019 FRQ 1
2016 FRQ 3
2018 FRQ Question 4
2018 FRQ Question 3
2018 FRQ Question 2
2018 FRQ Q1
2017 FRQ 4
2017 FRQ 3
2017 FRQ Question 2
2017 FRQ 1 - Homework
2016 FRQ 4
2016 FRQ 2
2016 FRQ Q1
FRQ - 2D Arrays
FRQ - ArrayLists
2025 FRQ 4
2025 FRQ 3
2025 FRQ 2
2025 FRQ 1
FRQ - Classes
FRQ - Array
2023 FRQ 4
2022 FRQ 4
2022 FRQ 3
2022 FRQ 2
2022 FRQ 1
2021 FRQ 4
2021 FRQ 1
2015 FRQ 4
2015 FRQ 2
2015 FRQ 1
2015 FRQ 3
2014 FRQ Q2 - Writing a Class
2019 FRQ 3 - Delimiters
2014 FRQ 1
Cookie Clicker in Game Engine
RPN Calculator Lesson
Introduction to Java ML
Graph Heuristics - Data Structures
Graph Heuristics - Data Structures
Graph Heuristics
Collections
Calculator Enactment 2
Calculator Enactment 1
Sorts Part 2
Collectable Types and Collections
RPN Calculator - Interactive Lesson
Calculator Hack
Understanding Reverse Polish Notation (RPN)
Calculator Hack - Wayfinding
Calculator Hack - Tracking
Abstract Fibonaccii Hack
Data Types
Selection - Insertion Sort
Merge Sort
Search - Linear, Binary, HashMaps
Single Responsibility & API Chaining
Quant Trading Bot (Bank Game)
1 min read
- Quant Trading Bot
Quant Trading Bot
Ready to load data
Level 1
Step 1 of 8
Welcome
Mastery Check
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Glossary (quick definitions)
Lookback: how many past days the model can “see.”
Horizon: how many days ahead the model tries to predict.
Features: inputs (price/indicators/news signals) used to predict.
Label: what you’re trying to predict (future price or direction).
Train/Test split: hold-out data to evaluate generalization.
Hyperparameters: settings like lookback/horizon and model type.
Overfitting: learning noise instead of patterns.
Generalization: performance on unseen time periods.
Leakage: accidentally using future info in training.
Directional accuracy: makes sense only if the label is “up/down.”
MAE/RMSE: average / squared error in dollars (lower is better).
R²: explained variance (can be negative).
Random Forest: an ensemble of trees that reduces variance.
Linear Regression: interpretable baseline (often underfits).
LSTM: sequence model (stub in this demo).
Signal: model output transformed into a trading rule.
Current Price
--
Last updated --
Daily Change
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From selected period
Volume
--
From selected period
Period High
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From selected period
Price Chart
Volume
Indicator Parameters
MA Crossover
--
RSI Signal
--
MACD Signal
--
Technical Indicators
Machine Learning Configuration
Accuracy
--
MAE
--
RMSE
--
R² Score
--
ML Predictions
Overall Sentiment
--
News Volume (24h)
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Negative Ratio
--
Category Sentiment
Recent Headlines
Backtest Configuration
Total Return
--
Max Drawdown
--
Win Rate
--
Portfolio Performance
Trade History
Bank Balance
--
Portfolio Value
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Total Holdings
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Place Order
Current Holdings