Darwinex

Darwinex

Views:
2,906,511
Subscribers:
52,200
Videos:
733
Duration:
8:10:43:03
United Kingdom
United Kingdom

Darwinex is a British content creator on YouTube with over 52.2 thousand subscribers, publishing 733 videos which altogether total approximately 2.91 million views.

Created on ● Channel Link: https://www.youtube.com/channel/UC6aYa9XjWy-HmHhyp5uN_9g





Top 100 Most Liked Videos by Darwinex


Video TitleRatingCategoryGame
1.Comparing On-Balance Volume, Money Flow Index, and Accumulation/Distribution1,630
2.On-Balance Volume (OBV) Indicator Trading Examples1,581
3.Accumulation / Distribution Versus OBV | Which Indicator is Best?1,005
4.Build Algorithmic Trading Strategies with Python & ZeroMQ: Part 1978
5.Money Flow Index Explained | A Volume-Based Indicator836
6.Money Flow Index | Trading Divergences and Overbought/Oversold694
7.How to Interface Python/R Algorithmic Trading Strategies with MetaTrader 4657Guide
8.How the On-Balance Volume (OBV) Indicator can Improve your Trading Strategy641
9.18) Using the Aroon Technical Indicator as a Market Regime Trend Filter630
10.How the RVOL Indicator Informs Breakout Strategies & Helps Avoid False Breakouts605Let's Play
11.VaR (Value at Risk), explained596
12.Anatomy of the FIX Protocol | FIX API for Algorithmic Trading @ Darwinex581
13.How the Market Facilitation Index can improve your trading strategies570
14.28) Lagging vs Leading Indicators & How To Use Them | SMA EMA KAMA MAVs568Guide
15.19) How to Develop Trading Systems using Trend Filters and Indicator Triggers542
16.25) How Triple Moving Averages Help Classify Market Regimes | Technical Trading526Let's Play
17.The Kaufman Adaptive Moving Average Indicator (KAMA)512
18.17) Using Trend Filters to Filter Out Low-Probability Trading Signals490
19.27) Build Algorithmic Trading Strategies by Combining Oscillators and Trend Following Indicators465
20.12.2) Using 'Walk Forward Optimization' to Improve Trading Results | Walk Forward Analysis459
21.12) Using Indicators for Probability-Based Predictions of Future Price Action in Trading Systems439
22.15) Using a 'percent-based' ATR (Average True Range) Volatility Filter424
23.26) Advanced Techniques to Categorize Trading Market Regimes | Algo Trading422
24.24) Using Dual Moving Averages to Identify Market Trends | Algo Trading415
25.The KAMA Indicator Calculation | How and why it works | Kaufman Adaptive Moving Average412
26.Using Volume Data and Volume Indicators to Improve Trading Decisions | Part 1 | Volume Basics410
27.20) Using Market Regime Filters in Systematic Trading Strategies406
28.1.4) The Probability Distribution of your Trading Edge | Algo Backtesting & Optimization Series401
29.How the RVOL Relative Volume Indicator can Improve your Trading Strategies | Part 6398
30.Algorithmic Trading via ZeroMQ: Python to MetaTrader (Trade Execution, Reporting & Management)393Tutorial
31.Indicator Examples | Market Facilitation Index | Trading Indicators in Practice393
32.Build Algorithmic Trading Strategies with Python & ZeroMQ: Part 2380
33.1.2) How to Code Multi-Symbol EAs (Expert Advisor) in MQL5 for MetaTrader (Strategy Tester and Live)372Guide
34.7.3) Determining Walk Forward vs Optimization length, using Statistical Significance369
35.13.2) Avoiding Pitfalls when using Walk Forward Analysis / Optimization (WFO/WFA)362
36.Improving Trading System Performance using Timeframe Filtering | Research Results 2360
37.23) How Algorithmic Traders Can Identify Market Trends | Moving Averages355
38.21) Using Trend-Following Indicators (SMA, EMA,...) in Systematic Trading Strategies353
39.Using the RVOL Indicator to aid Trend and Trading Range Predictions | Part 7345
40.14) Using Market Regime Volatility Filters to improve Trading System Results345
41.9.2) Using Indicators to Build Algo Trading Systems | Best-Practice Approach345
42.1.1) Trading for a Living | Making it all worthwhile and reaching your full potential.342
43.17.2) Research Results: Walk Forward Optimization Benefits340
44.9.1) How to Develop Algo Trading Systems using Indicators339Guide
45.16) Matching Volatility Filters with Timeframes of Trade Open & Close Triggers332
46.15.1) Research Study | Which Optimization Performance Metric is best? (PART 1)329
47.Trading Volume Spikes | What do they mean and how to trade them? | Volume Indicators319
48.13) Developing Trading Systems by Combining Technical Indicator Triggers with 'Market Type' Filters317
49.Building Volume Data Analysis into Trading Strategies | Part 2 | Basic Volume Principles316
50.4.2) Improve Optimization Statistical Significance with Multi-Symbol & Multi-Timeframe Backtesting315
51.16.1) Sharpe Ratio, Recovery Factor, Return/Max Drawdown, Expected Payoff.. Which is best? (PART 4)312
52.1.1) Why a true trading edge is a scarce commodity | Algorithmic Backtesting & Optimization Series312
53.Using the Efficiency Ratio to Measure Market Noise | Real-world Trading Strategies310
54.1.2) Is your trading backtest a 'Stochastic Illusion' or a 'Real Edge'? | Algo Optimization Series308
55.Measuring Market Noise using 'Price Density' | Improving Trading Strategies304
56.RSI vs Stochastic RSI Results - Which is the better indicator for O/B O/S Trading Strategies?301
57.Understanding Market Noise | Increase your Trading Strategy's Edge301
58.The Market Facilitation Index Calculation and the Indicator Signals it Provides297
59.41) Correlation between Stock Indices, FX and Commodities | A Macro-Economic Study296
60.1.3) Random vs Long-Term Edge Example | Algorithmic Backtesting & Optimization for Alphas293Let's Play
61.12.1) What is 'Walk Forward Analysis' and how does it improve Trading System Optimizations291
62.22) Issues with Moving Average Indicators in Algo Trading Strategies290
63.13.1) Walk Forward Analysis Best-Practice | Improving your Trading Strategy Optimizations287
64.Which Technical Indicators Provide a Genuine Trading Edge?287
65.5.1) The Right Way to Develop Algorithmic Trading Systems | Algo Trading for a Living286
66.Designing Profit-Taking Exits for Trading Strategies282
67.Entrevista al DARWIN ZVQ o cómo ganar durante 10 años consecutivos280
68.10) Technical Indicators - Take this Trading Challenge... If you dare!279
69.Improve Trading Strategy Edge using Volume Patterns | Part 3 | Volume Data Mini-Series279
70.Systematic Trading Clues to a Trend Finishing and a Trading Range Starting | Volume Indicators278
71.Initiating FIX Sessions (Logon 35=A) | FIX API for Algorithmic Trading @ Darwinex277Vlog
72.5.3) Avoid Live News to Protect Trading Systems | Algorithmic Backtesting & Optimization for Alphas271Let's Play
73.Bienvenido al movimiento de traders independientes270
74.11) Improve Trading Results with proper use of Technical Indicators | Training Improvement Process269
75.3.3) Recognising 'Fake Edge' in Trading System Optimizations - Improve robustness in your backtests268
76.Improve Trading Edge using a Price Density Noise Filter | Research Results 5268
77.3 techniques to improve your trading strategy using market noise267
78.3.1) Has your optimization overfitted your trading system? Is it really making accurate predictions?265
79.1.1) Why you REALLY need to start using Multi-Symbol Expert Advisors in MetaTrader 5262
80.1.3) Advanced MQL Techniques - Coding a Multi-Symbol Expert Advisor (EA) for MetaTrader 5256
81.1.3) Algo Trading for a Living | How to increase the profit-making potential of your capital255Guide
82.Asset Filtering using the Kaufman Efficiency Ratio253
83.4.1) Practical Steps to avoid Over-Fitting | Algorithmic Backtesting & Optimization for Alphas252Let's Play
84.Developing a Profitable Trend-Continuation Trading System246
85.How the Kaufman Efficiency Ratio Improves Ichimoku Strategy Performance by Avoiding Noise246
86.29) Introduction to Diversification | Reducing Risk by Portfolio Trading245
87.11.2) Understanding Tick Data, M1 OHLC, and Open Price Models in the MetaTrader Strategy Tester245
88.9.3) Using 'Return / Max Drawdown' and Normalized Profit Factor Performance Criteria | Backtesting244
89.15.2) Profit Factor, Return/Max Drawdown, Sharpe Ratio, Expected Payoff.. which is best? (PART 2)244
90.11.1) How to Ensure your Backtest Results are Indicative of Future Live Trading Performance243Guide
91.5.3) Algo Trading System Development: Best Practices to Improve Results242
92.9) MT5 Strategy Tester Agents | Multi-Threaded Backtesting239
93.The impact on Trading Strategies of Noise in different Timeframes238
94.Algorithmic Trading via ZeroMQ: Python to MetaTrader (Subscribing to Market Data)238
95.12) How to Configure Local Network Farm Agents in the MT5 Strategy Tester237Guide
96.8) Migrating MQL4 to MQL5 | Configuring Indicators in your EA (Expert Advisor)237
97.2.4) How to Extract Optimal Parameter Values in Optimizations using Statistical Power Analysis229Guide
98.Estimating Trading Strategy Returns | Van Tharp's Expectancy Metric226
99.4.3) Why your trading system can lose its edge | 14 Algo Trading Hints and Tips225
100.Improve Trading Success by understanding Price Action | Volatility and Noise225