🎯 Your Mission
MARKET TERMINAL v2.0
OBJECTIVE: You're a quantitative analyst at a hedge fund. Using 2 years of historical data, predict next month's stock movements and build a portfolio that beats the market.
WARNING: Markets are affected by earnings, news, and Black Swan events. Your model must handle volatility!
BENCHMARK: S&P 500 Index | TARGET: Outperform by >5%
🎮 Game Phases
📊 Phase 1: Data Analysis
Explore 2 years of historical data
- Identify trends and patterns
- Detect seasonality
- Find correlations
- Spot anomalies
Time: 10 minutes
🤖 Phase 2: Model Building
Create your prediction model
- Choose algorithms (ARIMA, LSTM, etc.)
- Feature engineering
- Train on historical data
- Validate predictions
Time: 15 minutes
💼 Phase 3: Portfolio Construction
Build your investment strategy
- Select stocks to buy/short
- Allocate capital
- Set stop-losses
- Define exit strategy
Time: 10 minutes
⚡ Phase 4: Black Swan Event!
Sudden market shock hits!
- Major news breaks
- Market drops 5-10%
- Adjust strategy quickly
- Minimize losses or capitalize
Time: 5 minutes
📊 Sample Portfolio Performance
Your Portfolio vs. Market
💥 Market Events You'll Face
Major tech company reports earnings 30% above estimates. Sector rallies.
Fed unexpectedly raises rates by 50 basis points. Growth stocks tumble.
Major bank collapses. Contagion fears spread. Circuit breakers triggered.
🎯 Prediction Strategies
📊 Technical Analysis
Use moving averages, RSI, MACD
Pros: Simple, visual, widely used
Cons: Lagging indicators, false signals
Best for: Trending markets
🤖 Machine Learning
LSTM, Random Forest, XGBoost
Pros: Captures complex patterns
Cons: Overfitting risk, black box
Best for: High-frequency patterns
📈 ARIMA Models
Time series decomposition
Pros: Solid statistical foundation
Cons: Assumes stationarity
Best for: Stable patterns
💡 Sentiment Analysis
News, social media, analyst ratings
Pros: Leading indicator
Cons: Noisy, manipulation
Best for: Event-driven moves
🎲 Monte Carlo
Probabilistic simulations
Pros: Risk quantification
Cons: Computationally intensive
Best for: Portfolio optimization
⚖️ Mean Reversion
Bet on return to average
Pros: Works in range-bound markets
Cons: Fails in trends
Best for: Oversold/overbought
📊 Scoring System
| Metric | Calculation | Points |
|---|---|---|
| Absolute Return | Final value / Initial value - 1 | Up to 300 points |
| Beat the Market | Your return - S&P 500 return | Up to 200 points |
| Risk-Adjusted Return | Sharpe Ratio (return/volatility) | Up to 200 points |
| Black Swan Response | Loss mitigation during crisis | Up to 150 points |
| Prediction Accuracy | Directional accuracy % | Up to 100 points |
| Strategy Documentation | Clear explanation of approach | Up to 50 points |
🏆 Leaderboard Categories
Market Beater
Highest total return
+500 XP
Risk Manager
Best Sharpe ratio
+400 XP
Prophet
Most accurate predictions
+300 XP
Crisis Navigator
Best Black Swan response
+400 XP
💡 Pro Tips
📈 Trend Following
"The trend is your friend until the end." Don't fight momentum, but have exit strategy ready.
🎲 Diversification
Never put all capital in one prediction. Spread risk across uncorrelated assets.
⚡ Volatility Clusters
Big moves follow big moves. Adjust position sizes when volatility increases.
📰 News Impact
Markets often overreact to news. Fade extreme moves for mean reversion plays.
🔄 Regime Changes
Models trained on bull markets fail in bear markets. Detect regime shifts.
🛑 Stop Losses
Always define maximum loss. Better to lose 5% than watch it become 50%.
📈 Ready to Beat the Market?
Test your prediction skills against real market dynamics!