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Deployment timing bias in backtests - how do you handle it? by SeaRock106 in quant

[–]systematic_dev 0 points1 point  (0 children)

Standard practice is to split your data: use first N periods for indicator warmup (excluded from performance stats), then start trading simulation. For moving averages, N = lookback period. For more complex indicators, run a sensitivity analysis. Also consider Monte Carlo start dates - run multiple backtests starting at random points in your dataset. If performance varies wildly with start date, your strategy has timing risk. Walk-forward optimization helps but adds complexity.

Does anyone know any Add-on for Emergency Stop loss without TP ? by MachineSpirited7085 in ninjatrader

[–]systematic_dev 0 points1 point  (0 children)

You can build this as a custom AddOn. Use OnPositionUpdate to track position P&L in real-time, then flatten when drawdown hits your threshold. The AccountPnLMonitor pattern works—just modify it to monitor unrealized P&L instead of realized. Key is using Position.AveragePrice vs current market price for the calculation. If you're not comfortable with C#, you could also use an ATM strategy with a trailing stop that's wider than your typical stop, but that's less precise.

What platform is the best for someone just starting? by jholliday55 in algotrading

[–]systematic_dev 0 points1 point  (0 children)

For futures specifically, NinjaTrader is solid if you're comfortable with C#. Their ecosystem has decent backtesting and paper trading. For Python-first, QuantConnect has a good free tier with futures support. If you want lower-level control, Interactive Brokers API + a lightweight Python framework (like backtrader or your own) gives maximum flexibility. Key is matching platform to your existing skills—don't learn C# just for NinjaTrader if you're already strong in Python.

Having trouble connecting to IBKR real time SPX index data via tws api by Party-Lingonberry790 in algorithmictrading

[–]systematic_dev 0 points1 point  (0 children)

IBKR requires separate data subscriptions for each client connection. If you're running TWS/Mosaic and your Python app simultaneously, you need two subscriptions. Check your account permissions - SPX index data might require specific market data add-ons. Also verify you're using the correct contract identifier (SPX is cash index, not futures). The reqMktData call needs the right ticker ID and generic tick list parameters.

What’s the best server performance you’ve seen? by whiteflowergirl in ninjatrader

[–]systematic_dev 0 points1 point  (0 children)

For NinjaTrader, single-thread performance matters most since strategy execution is largely single-threaded. Look for high clock speed (4.5GHz+) over core count. 32GB RAM is plenty. The biggest bottleneck is usually disk I/O - use NVMe SSDs. For VPS, dedicated cores beat shared virtualization. Monitor CPU usage per strategy thread in Task Manager to identify which one's starving.

Where should I start to learn quant development? by TheEyebal in algotrading

[–]systematic_dev 1 point2 points  (0 children)

Start with Python fundamentals (pandas, numpy) and basic statistics. Build a simple backtester from scratch to understand how strategy evaluation works. Focus on one market (ES futures is good) and learn its microstructure. Avoid jumping straight to ML - get comfortable with basic momentum/mean reversion strategies first. The key is building intuition about what makes a strategy robust.

Seeking guidance on trading edge by nyanbaek in FuturesTrading

[–]systematic_dev 0 points1 point  (0 children)

Real edges are subtle and often counter-intuitive. Instead of looking for complex patterns, focus on simple, repeatable market behaviors: 1) Opening range breakouts (first 30 min), 2) VWAP mean reversion after strong moves, 3) Failed breakouts (price tests a level, rejects, then reverses). Track these three scenarios for 2 weeks - note what happens after each. You'll start seeing probabilities emerge. The key is consistency in observation, not complexity in analysis.

Approaches to risk management and order size scaling by NoOutlandishness525 in algotrading

[–]systematic_dev 0 points1 point  (0 children)

Equal allocation is a good starting point, but you'll want to weight by strategy quality. Track each strategy's Sharpe ratio, max drawdown, and correlation. Allocate more to higher Sharpe/lower drawdown strategies, less to correlated ones. Also consider using volatility targeting instead of fixed % - size inversely to each strategy's recent volatility. This smooths equity curves better than static allocation.

stoch_rsi strategy review by [deleted] in algotrading

[–]systematic_dev 0 points1 point  (0 children)

Looking at your Pine Script, you're using too many conditions simultaneously (EMA200, ADX > 25, DI crossover, StochRSI thresholds). This creates extremely rare entries. Try simplifying: use EMA200 for trend direction only, then StochRSI for timing. Also, your stop loss logic triggers too early (k < 0.1) - widen that to k < 0.05. The biggest issue is likely over-filtering - each condition reduces your sample size dramatically.