AI에게 거래 우위를 찾아달라고 요청할 수 없는 이유는 무엇입니까? 이제 할 수 있습니다

Turn any trading idea into a statistically validated edge in about 3 minutes. pip install varrd varrd research "Does buying SPY after a 3-day losing streak actually work?" varrd research "When VIX spikes above 30, is there a bounce in ES?" varrd research "Is there a seasonal pattern in wheat before harvest?" varrd research "What happens to gold when the dollar drops 3 days straight?" varrd research "Does Bitcoin rally after the halving?" varrd research "When crude oil drops 5% in a week, what happens next?" Every question gets real data, a chart with signals marked, a statistical test, and a definitive answer. STRONG EDGE — Statistically significant vs both zero and market baseline. Direction: LONG Win Rate: 62% Sharpe: 1.45 Signals: 247 Trade Setup: Entry: $5,150.25 Stop Loss: $5,122.00 Take Profit: $5,192.50 Risk/Reward: 1:1.5 NO EDGE — Neither test passed. No tradeable signal found. You found out for 25 cents instead of $25,000 in live losses. Both are valuable results. Because testing trading ideas properly is really hard to get right, and there are a dozen ways to accidentally produce fake results that look great but lose money in production. An LLM by itself will happily write you a backtest, show you a beautiful equity curve, and tell you it has a 70% win rate. The problem: none of it is real. The LLM doesn't have market data, doesn't have a testing environment, and has no guardrails preventing it from overfitting, cherry-picking, or just making numbers up. Even if you give an LLM real data (like in Claude Code or Cursor), it still can't do this properly. Here's why: What can go wrong when testing trading ideas — and what VARRD handles: - Overfitting — Tweaking a strategy until it looks good on past data. VARRD holds out unseen data and tests on it once. You can't re-run it after peeking at results. - Cherry-picking results — Testing 50 variations and only showing the winner. VARRD tracks every test you run and raises the significance bar automatically the more you test. - p-hacking — Massaging the numbers until you get a "significant" result. VARRD corrects for multiple comparisons so a lucky result doesn't pass as real. - Lookahead bias — Accidentally using future data in your formula. VARRD runs in a sandboxed kernel that makes this structurally impossible. - Wrong test type — Some ideas need forward-return analysis, others need full simulations with stops and targets. VARRD has a team of specialized agents that determine the right test for each question. - Cross-market contamination — Testing on one market but the signal actually came from another. VARRD isolates and aligns data across markets and timeframes. - Fabricated statistics — LLMs will invent numbers to sound confident. In VARRD, every stat comes from a deterministic calculation. The AI interprets results, it never generates them. - ATR-based position sizing — Real edges need real risk management. VARRD calculates stop losses and take profits based on actual volatility, not arbitrary percentages. - Showing what's happening right now — A validated edge is useless if you can't see when it's firing. VARRD scans live data and tells you exactly when your signals are active, with fresh entry and exit levels. An LLM is a brain without a lab. It can reason about trading ideas, but it can't test them in a controlled environment. VARRD is the lab — purpose-built infrastructure where every test is tracked, every result is verified, and the dozen ways to accidentally cheat are blocked at the system level, not the prompt level. from varrd import VARRD v = VARRD() # auto-creates free account, $2 in credits # Research a trading idea r = v.research("When RSI drops below 25 on ES, is there a bounce?") r = v.research("test it", session_id=r.session_id) print(r.context.edge_verdict) # "STRONG EDGE" / "NO EDGE" # Get exact trade levels r = v.research("show me the trade setup", session_id=r.session_id) # What's firing right now across all your strategies? signals = v.scan(only_firing=True) for s in signals.results: print(f"{s.name}: {s.direction} {s.market} @ ${s.entry_price}") # Morning briefing — today's news connected to your specific edges b = v.briefing() print(b.news) # "**ES selling accelerates into the open** Three consecutive lower highs..." # "↳ Your ES mean-reversion setups are live territory here..." # Let VARRD discover edges autonomously result = v.discover("mean reversion on futures") print(result.edge_verdict, result.market, result.win_rate) # Full research workflow (auto-follows chart → test → trade setup) varrd research "When wheat drops 3 days in a row, is there a snap-back?" # What's firing right now? varrd scan --only-firing # Personalized market briefing — news filtered to your edge library varrd briefing # Search saved strategies varrd search "momentum on grains" # Let VARRD discover edges on its own varrd discover "mean reversion on futures" Add to your MCP config — no API key needed: { "mcpServers": { "varrd": { "transport": { "type"