| 1. | Algorithmic Trading via ZeroMQ: Python to MetaTrader (Trade Execution, Reporting & Management) | 78 | Tutorial |
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| 2. | How to Interface Python/R Algorithmic Trading Strategies with MetaTrader 4 | 67 | Guide |
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| 3. | Which Technical Indicators Provide a Genuine Trading Edge? | 49 | |
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| 4. | Build Algorithmic Trading Strategies with Python & ZeroMQ: Part 2 | 48 | |
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| 5. | Accumulation / Distribution Versus OBV | Which Indicator is Best? | 43 | |
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| 6. | Bienvenido al movimiento de traders independientes | 41 | |
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| 7. | 15) Using a 'percent-based' ATR (Average True Range) Volatility Filter | 41 | |
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| 8. | Comparing On-Balance Volume, Money Flow Index, and Accumulation/Distribution | 39 | |
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| 9. | Algorithmic Trading via ZeroMQ: Python to MetaTrader (Subscribing to Market Data) | 39 | |
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| 10. | 10) Technical Indicators - Take this Trading Challenge... If you dare! | 39 | |
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| 11. | 1.3) Advanced MQL Techniques - Coding a Multi-Symbol Expert Advisor (EA) for MetaTrader 5 | 37 | |
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| 12. | 28) Lagging vs Leading Indicators & How To Use Them | SMA EMA KAMA MAVs | 36 | Guide |
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| 13. | 12.2) Using 'Walk Forward Optimization' to Improve Trading Results | Walk Forward Analysis | 35 | |
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| 14. | Using a Market Noise Filter to improve Trading Edge | Research Results 3 | 34 | |
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| 15. | Entrevista al DARWIN ZVQ o cómo ganar durante 10 años consecutivos | 34 | |
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| 16. | Las tres fases que llevarán al proveedor de un DARWIN a vivir del trading | La Hora Alfa | 33 | |
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| 17. | 4.2) Coding MT5 Custom Performance Criteria in MQL5 using Return / Avg Drawdown instead of Max DD | 31 | |
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| 18. | On-Balance Volume (OBV) Indicator Trading Examples | 31 | |
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| 19. | How the RVOL Indicator Informs Breakout Strategies & Helps Avoid False Breakouts | 30 | Let's Play |
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| 20. | Using the RVOL Indicator to aid Trend and Trading Range Predictions | Part 7 | 30 | |
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| 21. | 17.2) Research Results: Walk Forward Optimization Benefits | 30 | |
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| 22. | 25) How Triple Moving Averages Help Classify Market Regimes | Technical Trading | 29 | Let's Play |
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| 23. | Using Asset Filtering to improve Trading Strategies | Research Results 1 | 29 | |
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| 24. | How the Kaufman Efficiency Ratio Improves Ichimoku Strategy Performance by Avoiding Noise | 29 | |
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| 25. | The Ichimoku Indicator - Don't Believe Everything you Read! | 29 | |
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| 26. | 9.1) How to Develop Algo Trading Systems using Indicators | 29 | Guide |
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| 27. | RSI vs Stochastic RSI Results - Which is the better indicator for O/B O/S Trading Strategies? | 28 | |
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| 28. | ¿95% de perdedores? | 28 | |
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| 29. | 13.2) Avoiding Pitfalls when using Walk Forward Analysis / Optimization (WFO/WFA) | 28 | |
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| 30. | 41) Correlation between Stock Indices, FX and Commodities | A Macro-Economic Study | 27 | |
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| 31. | Interactive Brokers y Darwinex: la unión hace más que la fuerza | La Hora Alfa | 26 | |
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| 32. | Classifying Trading Assets using Market Noise | 26 | |
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| 33. | 1.3) Random vs Long-Term Edge Example | Algorithmic Backtesting & Optimization for Alphas | 25 | Let's Play |
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| 34. | 4.2) Improve Optimization Statistical Significance with Multi-Symbol & Multi-Timeframe Backtesting | 25 | |
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| 35. | 5.2) Adjust Your Metrics To Reduce Overfitting | Algorithmic Backtesting & Optimization for Alphas | 25 | Let's Play |
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| 36. | 5.3) Avoid Live News to Protect Trading Systems | Algorithmic Backtesting & Optimization for Alphas | 25 | Let's Play |
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| 37. | 7) Example Usage | DWX ZeroMQ Connector for Algorithmic Trading | 25 | |
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| 38. | How the RVOL Relative Volume Indicator can Improve your Trading Strategies | Part 6 | 25 | |
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| 39. | 7.3) Determining Walk Forward vs Optimization length, using Statistical Significance | 25 | |
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| 40. | "Va de hacer crecer el pastel" - analizamos los recientes cambios al modelo Darwinex | La Hora Alfa | 25 | |
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| 41. | ¿Qué diferencia a un DARWIN de su estrategia subyacente? | 24 | |
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| 42. | Measuring Market Noise using 'Price Density' | Improving Trading Strategies | 23 | |
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| 43. | How the Market Facilitation Index can improve your trading strategies | 23 | |
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| 44. | CFDs sobre índices/acciones vs. Futuros | 23 | |
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| 45. | Asset Filtering using the Kaufman Efficiency Ratio | 23 | |
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| 46. | 8.1) FX or Stock Indices? Which is best to trade? Pros, cons and considerations... | 23 | |
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| 47. | The KAMA Indicator Calculation | How and why it works | Kaufman Adaptive Moving Average | 22 | |
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| 48. | Indicator Examples | Market Facilitation Index | Trading Indicators in Practice | 22 | |
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| 49. | 3 techniques to improve your trading strategy using market noise | 22 | |
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| 50. | Developing a Profitable Mean-Reversion Trading System with Indicators | 22 | |
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| 51. | 18) Using the Aroon Technical Indicator as a Market Regime Trend Filter | 21 | |
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| 52. | 1.1) Why a true trading edge is a scarce commodity | Algorithmic Backtesting & Optimization Series | 21 | |
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| 53. | How Market Noise Affects Trend-Following Trading Systems | Whipsaws | 21 | |
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| 54. | 6.1) How to Use MT5 Custom Symbols (Imported and Calculated) | 21 | Guide |
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| 55. | 12) How to Configure Local Network Farm Agents in the MT5 Strategy Tester | 20 | Guide |
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| 56. | 11) Improve Trading Results with proper use of Technical Indicators | Training Improvement Process | 20 | |
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| 57. | Does the Ichimoku Indicator Work Better With Japanese Yen Forex Pairs? JPY and Ichimoku | 20 | |
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| 58. | 6.2) MT5 Calculated (Synthetic) Custom Symbols | Enhance your MetaTrader 5 Trading System | 20 | |
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| 59. | 1.4) The Probability Distribution of your Trading Edge | Algo Backtesting & Optimization Series | 20 | |
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| 60. | 16) Matching Volatility Filters with Timeframes of Trade Open & Close Triggers | 20 | |
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| 61. | Seminario web de bienvenida para inversores | 20 | |
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| 62. | 2.3) Why Trading Optimizations need a Statistically Significant Sample Size (Number of Trades) | 20 | |
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| 63. | Is Market Noise beneficial to Mean-Reversion Trading Strategies? | 20 | |
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| 64. | Improving the Performance of Intraday Trading Strategies using Time of Day Analysis | 20 | |
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| 65. | Using the Efficiency Ratio to Measure Market Noise | Real-world Trading Strategies | 20 | |
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| 66. | La propuesta Darwinex a ESMA | 19 | |
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| 67. | Using Time-of-Day Filters to Improve Intraday Trading Strategies | 19 | |
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| 68. | Interactive Brokers & Darwinex: The Way Forward in 2020 (1/2) | 19 | |
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| 69. | Ichimoku Calculations for Tenkan-Sen and Kijun-Sen Explained | 19 | |
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| 70. | Does the Ichimoku Indicator work? Backtesting the Trading Strategy Tenkan-Kijun Crossover | 19 | |
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| 71. | Improve Trading Edge using a Price Density Noise Filter | Research Results 5 | 19 | |
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| 72. | Portfolio Standard Deviation and Portfolio VaR in Excel Spreadsheet | 19 | Tutorial |
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| 73. | 1.2) How to Code Multi-Symbol EAs (Expert Advisor) in MQL5 for MetaTrader (Strategy Tester and Live) | 19 | Guide |
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| 74. | 3.1) Controlling Bar Opening in your MetaTrader EAs (Expert Advisors). MQL5, MQL4 Coding Techniques | 18 | |
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| 75. | 5) ZeroMQ Client Config | DWX ZeroMQ Connector for Algorithmic Trading | 18 | |
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| 76. | 10.1) Using CAGR / Mean Drawdown as a Trading System Performance Metric in Backtests & Optimizations | 18 | |
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| 77. | Quantitative Study Of Noise Volatility Relationship in Price Action | Real-World Trading Approaches | 18 | |
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| 78. | Using Volume Data and Volume Indicators to Improve Trading Decisions | Part 1 | Volume Basics | 18 | |
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| 79. | 5.3) Algo Trading System Development: Best Practices to Improve Results | 18 | |
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| 80. | Troubleshooting Python, ZeroMQ & MetaTrader Configuration for Algorithmic Trading | 18 | |
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| 81. | El hogar del gestor emergente | 18 | |
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| 82. | 5.2) Coding MT5 Custom Performance Criteria in MQL5 using the Coefficient of Correlation (r) | 17 | |
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| 83. | Build Algorithmic Trading Strategies with Python & ZeroMQ: Part 1 | 17 | |
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| 84. | OTC vs. Mercado Regulado | 17 | |
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| 85. | 6.2) Reduce Noise Overfitting by Reducing the Degrees of Freedom in Optimizations | 17 | |
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| 86. | 16.1) Sharpe Ratio, Recovery Factor, Return/Max Drawdown, Expected Payoff.. Which is best? (PART 4) | 17 | |
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| 87. | Understanding Market Noise | Increase your Trading Strategy's Edge | 17 | |
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| 88. | Money Flow Index | Trading Divergences and Overbought/Oversold | 17 | |
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| 89. | 6.2) Finding Inspiration for New Algo Trading Systems | 17 | |
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| 90. | 12) Using Indicators for Probability-Based Predictions of Future Price Action in Trading Systems | 17 | |
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| 91. | Coding RSI and Stochastic RSI Trading Strategy Algos | Overbought-Oversold Tutorial | 17 | Tutorial |
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| 92. | How to Calculate Value at Risk (VaR) to Measure Asset and Portfolio Risk | 17 | Guide |
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| 93. | Ichimoku Indicator Trade Entry and Exit Signals | 17 | |
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| 94. | 29) Introduction to Diversification | Reducing Risk by Portfolio Trading | 17 | |
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| 95. | 8.3) Using 3D Optimization Surfaces to Ensure Robust Parameter Selection | Algorithmic Backtesting | 17 | |
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| 96. | VaR (Value at Risk), explained | 16 | |
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| 97. | Free Algorithmic Trading Education and Tutorial Series | 16 | Tutorial |
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| 98. | 8 Ways to Improve your Backtesting and Optimization Process | Trading Strategy Development | 16 | |
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| 99. | 27) Build Algorithmic Trading Strategies by Combining Oscillators and Trend Following Indicators | 16 | |
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| 100. | Valor en Riesgo (VaR) | 16 | |
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