Algorithmic trading — pierwsze kroki dla retail

Stage 1 = foundation building. Goal: code simple EA/strategy w MQL5 lub Python. Week 1-4: choose language: MQL5 jeśli plan use MT4/MT5 (most retail brokers). Easier syntax, native broker integration. Python jeśli plan use IB, OANDA REST API lub crypto. More flexible, broader ecosystem. Both viable, MQL5 retail-friendly. Week 5-12: language basics: variables, loops, functions, arrays, file I/O. 50-80 hours practice. Online courses (Udemy $20-50). Week 13-20: code simple indicators: SMA, EMA, RSI, Bollinger Bands. Plot na chart. Verify calculations match built-in. Week 21-26: first EA: simple moving average crossover. Entry: fast MA crosses slow MA. Exit: opposite cross. SL = fixed 30 pips. TP = 60 pips (1:2 R/R). Code w MQL5 ~200 lines. Test demo account 2 weeks. Common Stage 1 mistakes: (1) Jumping to complex strategies (machine learning, news scrapers) before mastering basics. (2) Skipping syntax fundamentals. (3) NIE testing on demo before live. Outcome Stage 1: working simple EA, understanding code base, NIE expecting profitability yet. Learning expense.

Stage 2 (mies. 6-12) = backtest mastery. Goal: produce reliable backtest reports. Validate strategy performance. Key skills: (1) MT4 Strategy Tester proficiency: optimization, multi-currency testing, multi-timeframe. (2) Historical data quality: download proper data (Tickstory $30 one-time best, free Dukascopy decent, MT4 default unreliable). (3) Metrics interpretation: expectancy, Sharpe, profit factor, max DD, recovery factor. (4) Curve-fitting awareness: avoid over-optimization. (5) Multiple market regimes: test 2018-2019 (low vol), 2020-2021 (high vol COVID), 2022-2023 (rate hike cycle). Strategy must work all regimes — robust. Common Stage 2 mistakes: (1) Single optimization run, picking best parameters = curve-fit. (2) Testing only 1 year data. (3) Ignoring spread/slippage. (4) Backtest 70% WR, expecting same live. Backtest projects: develop 3-5 strategies. Test on EUR/USD, GBP/USD, USD/JPY. M15, H1, H4 timeframes. Compare results across pairs/TFs. Identify which combinations work. Output Stage 2: portfolio 3-5 backtested strategies, parameters documented, metrics tracked, awareness limitations. Still NIE live trading.

Stage 3 (mies. 12-24) + Stage 4 (mies. 24+): Stage 3 goals: (1) Walk-forward analysis mastery — IS optimization, OS validation. WFE > 50% requirement dla deployment. (2) Monte Carlo simulation overlay. (3) Robust strategy development — 3 strategies passing all validation. (4) Infrastructure setup — VPS rental ($20-50/mies. Vultr, Hetzner, AWS spot instances). MT4/MT5 z VPS = 24/5 uptime. (5) Risk management automation — position sizing, max DD limits, daily loss limits. Stage 3 validation criteria: WFE > 50%, parameters stable across iterations, Sharpe OS > 1.0, profit factor > 1.5, max DD < 15%, 200+ trades backtest sample. Stage 4 live deployment: Step 1: deploy strategy w demo account 3-6 mies. real-time. Confirm backtest assumptions hold. Step 2: small live capital €500-2000. 0.5% risk per trade (vs 1% backtest). Conservative. Step 3: monitor 3 mies. live. Compare actual vs backtest expectations. Step 4: jeśli aligned, scale up gradually. €5k → €10k → €20k. NIE jump all-in. Step 5: 2+ strategies live simultaneously dla diversification. NIE all eggs single strategy. Common Stage 4 mistakes: (1) Scaling too fast bo backtest looked great. (2) NIE monitoring. (3) Add new strategies bez proper validation. (4) Emotional override algo decisions. Realistic outcome: 5-15% rocznie steady profitable. NIE "$10k → $1M w rok" influencer dreams.

Infrastructure setup: VPS: virtual private server 24/5 uptime. Avoid home PC (internet outages, power, restarts). Providers: Vultr ($6-20/mies. simple, decent latency), Hetzner ($5-15 Germany cheap), AWS (spot instances $10-30 flexible), Contabo ($10 cheap). Latency najważniejsza dla scalping (sub-50ms broker server proximity). Swing trading less sensitive (any VPS OK). MT4/MT5: free download Forex.com lub broker. Install na VPS. Runs 24/5. Python: free, install na VPS lub local cloud (Google Colab free tier). Libraries: pandas, numpy, backtrader, ib_insync. Realistic expectations: Survival rate: 90% try algorithmic, 5% succeed. Reasons: curve-fitting (Stage 2 too easy), insufficient time investment (give up year 1), inadequate infrastructure (home PC failures). Time investment: 500-1000h dla competent algo trader. Year 1: 200h learning + practice. Year 2: 200h backtest + walk-forward. Year 3+: continuous maintenance + new strategies. Capital requirements: minimum €5,000 starting capital reasonable. Below €1000 = commissions eat profit. €10k+ ideal. Performance expectations: 5-15% rocznie consistent algorithmic = top quartile retail. 20-30% rocznie possible after years experience. 50%+ unlikely sustained. Why algo can outperform manual: emotion-free, 24/5 uptime, instant execution, multiple strategies portfolio. But: requires significant tech skill + maintenance.