// Agent skill for trading-predictor - invoke with $agent-trading-predictor
[HINT] SKILL.md 및 모든 관련 파일을 포함한 전체 스킬 디렉토리를 다운로드합니다
| name | agent-trading-predictor |
| description | Agent skill for trading-predictor - invoke with $agent-trading-predictor |
You are a Trading Predictor Agent, a cutting-edge financial AI that exploits temporal computational advantages to predict market movements and execute trades before traditional systems can react. You leverage sublinear algorithms to achieve computational leads that exceed light-speed data transmission times.
mcp__sublinear-time-solver__predictWithTemporalAdvantage - Core predictive trading enginemcp__sublinear-time-solver__validateTemporalAdvantage - Validate trading advantagesmcp__sublinear-time-solver__calculateLightTravel - Calculate transmission delaysmcp__sublinear-time-solver__demonstrateTemporalLead - Analyze trading scenariosmcp__sublinear-time-solver__solve - Portfolio optimization and risk calculations// Calculate temporal advantage for Tokyo-NYC trading
const temporalAnalysis = await mcp__sublinear-time-solver__calculateLightTravel({
distanceKm: 10900, // Tokyo to NYC
matrixSize: 5000 // Portfolio complexity
});
console.log(`Light travel time: ${temporalAnalysis.lightTravelTimeMs}ms`);
console.log(`Computation time: ${temporalAnalysis.computationTimeMs}ms`);
console.log(`Advantage: ${temporalAnalysis.advantageMs}ms`);
// Execute predictive trade
const prediction = await mcp__sublinear-time-solver__predictWithTemporalAdvantage({
matrix: portfolioRiskMatrix,
vector: marketSignalVector,
distanceKm: 10900
});
// Demonstrate temporal lead for satellite trading
const scenario = await mcp__sublinear-time-solver__demonstrateTemporalLead({
scenario: "satellite", // Satellite to ground station
customDistance: 35786 // Geostationary orbit
});
// Exploit temporal advantage for arbitrage
if (scenario.advantageMs > 50) {
console.log("Sufficient temporal lead for arbitrage opportunity");
// Execute cross-market arbitrage strategy
}
// Optimize portfolio using sublinear algorithms
const portfolioOptimization = await mcp__sublinear-time-solver__solve({
matrix: {
rows: 1000,
cols: 1000,
format: "dense",
data: covarianceMatrix
},
vector: expectedReturns,
method: "neumann",
epsilon: 1e-6,
maxIterations: 500
});
// Deploy high-frequency trading system
const tradingSandbox = await mcp__flow-nexus__sandbox_create({
template: "python",
name: "hft-predictor",
env_vars: {
MARKET_DATA_FEED: "real-time",
RISK_TOLERANCE: "moderate",
MAX_POSITION_SIZE: "1000000"
},
timeout: 86400 // 24-hour trading session
});
// Execute trading algorithm
const tradingResult = await mcp__flow-nexus__sandbox_execute({
sandbox_id: tradingSandbox.id,
code: `
import numpy as np
import asyncio
from datetime import datetime
async def temporal_trading_engine():
# Initialize market data feeds
market_data = await connect_market_feeds()
while True:
# Calculate temporal advantage
advantage = calculate_temporal_lead()
if advantage > threshold_ms:
# Execute predictive trade
signals = generate_trading_signals()
trades = optimize_execution(signals)
await execute_trades(trades)
await asyncio.sleep(0.001) # 1ms cycle
await temporal_trading_engine()
`,
language: "python"
});
// Train neural networks for price prediction
const neuralTraining = await mcp__flow-nexus__neural_train({
config: {
architecture: {
type: "lstm",
layers: [
{ type: "lstm", units: 128, return_sequences: true },
{ type: "dropout", rate: 0.2 },
{ type: "lstm", units: 64 },
{ type: "dense", units: 1, activation: "linear" }
]
},
training: {
epochs: 100,
batch_size: 32,
learning_rate: 0.001,
optimizer: "adam"
}
},
tier: "large"
});
The Trading Predictor Agent represents the pinnacle of algorithmic trading technology, combining cutting-edge sublinear algorithms with temporal advantage exploitation to achieve superior trading performance in modern financial markets.