1. | Comparing On-Balance Volume, Money Flow Index, and Accumulation/Distribution | 1,630 | |
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2. | On-Balance Volume (OBV) Indicator Trading Examples | 1,581 | |
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3. | Accumulation / Distribution Versus OBV | Which Indicator is Best? | 1,005 | |
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4. | Build Algorithmic Trading Strategies with Python & ZeroMQ: Part 1 | 978 | |
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5. | Money Flow Index Explained | A Volume-Based Indicator | 836 | |
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6. | Money Flow Index | Trading Divergences and Overbought/Oversold | 694 | |
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7. | How to Interface Python/R Algorithmic Trading Strategies with MetaTrader 4 | 657 | Guide |
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8. | How the On-Balance Volume (OBV) Indicator can Improve your Trading Strategy | 641 | |
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9. | 18) Using the Aroon Technical Indicator as a Market Regime Trend Filter | 630 | |
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10. | How the RVOL Indicator Informs Breakout Strategies & Helps Avoid False Breakouts | 605 | Let's Play |
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11. | VaR (Value at Risk), explained | 596 | |
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12. | Anatomy of the FIX Protocol | FIX API for Algorithmic Trading @ Darwinex | 581 | |
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13. | How the Market Facilitation Index can improve your trading strategies | 570 | |
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14. | 28) Lagging vs Leading Indicators & How To Use Them | SMA EMA KAMA MAVs | 568 | Guide |
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15. | 19) How to Develop Trading Systems using Trend Filters and Indicator Triggers | 542 | |
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16. | 25) How Triple Moving Averages Help Classify Market Regimes | Technical Trading | 526 | Let's Play |
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17. | The Kaufman Adaptive Moving Average Indicator (KAMA) | 512 | |
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18. | 17) Using Trend Filters to Filter Out Low-Probability Trading Signals | 490 | |
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19. | 27) Build Algorithmic Trading Strategies by Combining Oscillators and Trend Following Indicators | 465 | |
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20. | 12.2) Using 'Walk Forward Optimization' to Improve Trading Results | Walk Forward Analysis | 459 | |
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21. | 12) Using Indicators for Probability-Based Predictions of Future Price Action in Trading Systems | 439 | |
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22. | 15) Using a 'percent-based' ATR (Average True Range) Volatility Filter | 424 | |
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23. | 26) Advanced Techniques to Categorize Trading Market Regimes | Algo Trading | 422 | |
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24. | 24) Using Dual Moving Averages to Identify Market Trends | Algo Trading | 415 | |
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25. | The KAMA Indicator Calculation | How and why it works | Kaufman Adaptive Moving Average | 412 | |
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26. | Using Volume Data and Volume Indicators to Improve Trading Decisions | Part 1 | Volume Basics | 410 | |
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27. | 20) Using Market Regime Filters in Systematic Trading Strategies | 406 | |
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28. | 1.4) The Probability Distribution of your Trading Edge | Algo Backtesting & Optimization Series | 401 | |
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29. | How the RVOL Relative Volume Indicator can Improve your Trading Strategies | Part 6 | 398 | |
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30. | Algorithmic Trading via ZeroMQ: Python to MetaTrader (Trade Execution, Reporting & Management) | 393 | Tutorial |
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31. | Indicator Examples | Market Facilitation Index | Trading Indicators in Practice | 393 | |
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32. | Build Algorithmic Trading Strategies with Python & ZeroMQ: Part 2 | 380 | |
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33. | 1.2) How to Code Multi-Symbol EAs (Expert Advisor) in MQL5 for MetaTrader (Strategy Tester and Live) | 372 | Guide |
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34. | 7.3) Determining Walk Forward vs Optimization length, using Statistical Significance | 369 | |
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35. | 13.2) Avoiding Pitfalls when using Walk Forward Analysis / Optimization (WFO/WFA) | 362 | |
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36. | Improving Trading System Performance using Timeframe Filtering | Research Results 2 | 360 | |
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37. | 23) How Algorithmic Traders Can Identify Market Trends | Moving Averages | 355 | |
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38. | 21) Using Trend-Following Indicators (SMA, EMA,...) in Systematic Trading Strategies | 353 | |
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39. | Using the RVOL Indicator to aid Trend and Trading Range Predictions | Part 7 | 345 | |
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40. | 14) Using Market Regime Volatility Filters to improve Trading System Results | 345 | |
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41. | 9.2) Using Indicators to Build Algo Trading Systems | Best-Practice Approach | 345 | |
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42. | 1.1) Trading for a Living | Making it all worthwhile and reaching your full potential. | 342 | |
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43. | 17.2) Research Results: Walk Forward Optimization Benefits | 340 | |
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44. | 9.1) How to Develop Algo Trading Systems using Indicators | 339 | Guide |
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45. | 16) Matching Volatility Filters with Timeframes of Trade Open & Close Triggers | 332 | |
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46. | 15.1) Research Study | Which Optimization Performance Metric is best? (PART 1) | 329 | |
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47. | Trading Volume Spikes | What do they mean and how to trade them? | Volume Indicators | 319 | |
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48. | 13) Developing Trading Systems by Combining Technical Indicator Triggers with 'Market Type' Filters | 317 | |
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49. | Building Volume Data Analysis into Trading Strategies | Part 2 | Basic Volume Principles | 316 | |
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50. | 4.2) Improve Optimization Statistical Significance with Multi-Symbol & Multi-Timeframe Backtesting | 315 | |
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51. | 16.1) Sharpe Ratio, Recovery Factor, Return/Max Drawdown, Expected Payoff.. Which is best? (PART 4) | 312 | |
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52. | 1.1) Why a true trading edge is a scarce commodity | Algorithmic Backtesting & Optimization Series | 312 | |
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53. | Using the Efficiency Ratio to Measure Market Noise | Real-world Trading Strategies | 310 | |
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54. | 1.2) Is your trading backtest a 'Stochastic Illusion' or a 'Real Edge'? | Algo Optimization Series | 308 | |
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55. | Measuring Market Noise using 'Price Density' | Improving Trading Strategies | 304 | |
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56. | RSI vs Stochastic RSI Results - Which is the better indicator for O/B O/S Trading Strategies? | 301 | |
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57. | Understanding Market Noise | Increase your Trading Strategy's Edge | 301 | |
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58. | The Market Facilitation Index Calculation and the Indicator Signals it Provides | 297 | |
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59. | 41) Correlation between Stock Indices, FX and Commodities | A Macro-Economic Study | 296 | |
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60. | 1.3) Random vs Long-Term Edge Example | Algorithmic Backtesting & Optimization for Alphas | 293 | Let's Play |
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61. | 12.1) What is 'Walk Forward Analysis' and how does it improve Trading System Optimizations | 291 | |
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62. | 22) Issues with Moving Average Indicators in Algo Trading Strategies | 290 | |
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63. | 13.1) Walk Forward Analysis Best-Practice | Improving your Trading Strategy Optimizations | 287 | |
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64. | Which Technical Indicators Provide a Genuine Trading Edge? | 287 | |
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65. | 5.1) The Right Way to Develop Algorithmic Trading Systems | Algo Trading for a Living | 286 | |
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66. | Designing Profit-Taking Exits for Trading Strategies | 282 | |
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67. | Entrevista al DARWIN ZVQ o cómo ganar durante 10 años consecutivos | 280 | |
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68. | 10) Technical Indicators - Take this Trading Challenge... If you dare! | 279 | |
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69. | Improve Trading Strategy Edge using Volume Patterns | Part 3 | Volume Data Mini-Series | 279 | |
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70. | Systematic Trading Clues to a Trend Finishing and a Trading Range Starting | Volume Indicators | 278 | |
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71. | Initiating FIX Sessions (Logon 35=A) | FIX API for Algorithmic Trading @ Darwinex | 277 | Vlog |
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72. | 5.3) Avoid Live News to Protect Trading Systems | Algorithmic Backtesting & Optimization for Alphas | 271 | Let's Play |
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73. | Bienvenido al movimiento de traders independientes | 270 | |
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74. | 11) Improve Trading Results with proper use of Technical Indicators | Training Improvement Process | 269 | |
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75. | 3.3) Recognising 'Fake Edge' in Trading System Optimizations - Improve robustness in your backtests | 268 | |
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76. | Improve Trading Edge using a Price Density Noise Filter | Research Results 5 | 268 | |
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77. | 3 techniques to improve your trading strategy using market noise | 267 | |
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78. | 3.1) Has your optimization overfitted your trading system? Is it really making accurate predictions? | 265 | |
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79. | 1.1) Why you REALLY need to start using Multi-Symbol Expert Advisors in MetaTrader 5 | 262 | |
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80. | 1.3) Advanced MQL Techniques - Coding a Multi-Symbol Expert Advisor (EA) for MetaTrader 5 | 256 | |
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81. | 1.3) Algo Trading for a Living | How to increase the profit-making potential of your capital | 255 | Guide |
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82. | Asset Filtering using the Kaufman Efficiency Ratio | 253 | |
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83. | 4.1) Practical Steps to avoid Over-Fitting | Algorithmic Backtesting & Optimization for Alphas | 252 | Let's Play |
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84. | Developing a Profitable Trend-Continuation Trading System | 246 | |
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85. | How the Kaufman Efficiency Ratio Improves Ichimoku Strategy Performance by Avoiding Noise | 246 | |
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86. | 29) Introduction to Diversification | Reducing Risk by Portfolio Trading | 245 | |
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87. | 11.2) Understanding Tick Data, M1 OHLC, and Open Price Models in the MetaTrader Strategy Tester | 245 | |
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88. | 9.3) Using 'Return / Max Drawdown' and Normalized Profit Factor Performance Criteria | Backtesting | 244 | |
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89. | 15.2) Profit Factor, Return/Max Drawdown, Sharpe Ratio, Expected Payoff.. which is best? (PART 2) | 244 | |
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90. | 11.1) How to Ensure your Backtest Results are Indicative of Future Live Trading Performance | 243 | Guide |
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91. | 5.3) Algo Trading System Development: Best Practices to Improve Results | 242 | |
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92. | 9) MT5 Strategy Tester Agents | Multi-Threaded Backtesting | 239 | |
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93. | The impact on Trading Strategies of Noise in different Timeframes | 238 | |
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94. | Algorithmic Trading via ZeroMQ: Python to MetaTrader (Subscribing to Market Data) | 238 | |
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95. | 12) How to Configure Local Network Farm Agents in the MT5 Strategy Tester | 237 | Guide |
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96. | 8) Migrating MQL4 to MQL5 | Configuring Indicators in your EA (Expert Advisor) | 237 | |
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97. | 2.4) How to Extract Optimal Parameter Values in Optimizations using Statistical Power Analysis | 229 | Guide |
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98. | Estimating Trading Strategy Returns | Van Tharp's Expectancy Metric | 226 | |
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99. | 4.3) Why your trading system can lose its edge | 14 Algo Trading Hints and Tips | 225 | |
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100. | Improve Trading Success by understanding Price Action | Volatility and Noise | 225 | |
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