
186 episodes

Better System Trader Andrew Swanscott chats with professional traders Larry Williams, Ernest Cha
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- Investing
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4.9 • 52 Ratings
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If you’re looking for inspiration, motivation and practical advice on improving your trading results, Better System Trader delivers every fortnight. Each episode brings you an expert trader who shares their own story, along with the steps, both good and bad, that they've taken on their path to success. With a focus on actionable insights, the tips and tricks used by the experts contain loads of value, providing you with insanely practical tips and tools you can start using TODAY. Improve your trading with Better System Trader.
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185: How to avoid creating trading strategies that degrade quickly - Timothy Masters
Numerical Computing specialist and author Timothy Masters joins us (in his only ever interview) to discuss trading strategy development and validation techniques, including:
Why trading strategies fall apart in live trading, How luck impacts trading strategy results and the impact of selecting the 'best' trading system, Why trades in an out of sample dataset are NOT representative of future trades, 3 ways to use Monte Carlo Permutation tests in trading strategy development to avoid overfitting, evaluate strategy robustness and assess the quality of your strategy development process, The danger of using out of sample trades to compute the probability of drawdowns, Entropy and information of indicators and why it's important to strategy development, How stationarity really impacts trading strategies and what strategy developers can do about it, Using Walk Forward to determine how robust a strategy is in the market, Incomplete Beta Distribution to track strategy deterioration, Bootstrapping, granular Profit Factor, the predictive ability of technical indicators, counter-trend trading and much more. -
184: Increasing returns using "Performance Qualifiers" – Tomas Nesnidal
Breakout trading specialist and Hedge Fund manager Tomas Nesnidal joins us to discuss how to increase the returns of trading strategies using "Performance Qualifiers" while also managing risk, including:
What is Dynamic Position Sizing (DPS) and how can it improve risk-adjusted returns, How Dynamic Position Sizing is different to classical position sizing (and why DPS is better), Why it’s important for traders to connect position size with what’s going on in the markets, instead of just their equity curve, The 5 groups of Performance Qualifiers and how to use them to identify the probabilities of future trades, Why using filters to improve trading strategies can be dangerous (and DPS is a much better option instead), Plus, Dynamic Position Sizing and risk management, Optimal-f and older position sizing techniques, Dynamic Position Sizing for stocks, forex and other markets, why it’s important for traders to try new concepts, and much more. ► Download the free DPS ebook at https://dpstradingtechnique.com or discover more from Tomas at https://systemsontheroad.com -
183: Overcoming Broken Strategies – Marsten Parker
Unknown market wizard Marsten Parker joins us to share how he overcame broken strategies and adapted his trading to deliver a 20-year track record most traders would envy, including:
Being featured in the “Unknown Market Wizards” book by Jack Schwager, The impacts of a 45% drawdown and how Marsten got back into trading, How to use a system stop to protect your trading account from large losses, Why it’s important to understand why a strategy works, Mean Reversion short strategies, why they’re dangerous and how to mitigate some of the risk, How to detect and protect yourself from a strategy that’s broken, Uncertainty in the market and the future performance of strategies, Equity curve trading, market speed, diversified strategies, tracking old strategies, indicators, market regimes and much more. -
182: How to identify high probability markets - David Steets
David Steets from Systematic Individual Investor joins us to discuss how to map market probabilities and identify high probability markets using "edge stacking" techniques, including:
The importance of mapping market probabilities, How to identify if something really contains an edge, Blending return probabilities across buckets to create a probability map, Classifying markets over multiple timeframes, How to confirm which edges are currently relevant, Using both technical and fundamental in models, The reliability of edges over time, Managing conflicting information and edges, Bear market signals, integrating discretion, weighting edges, VIX term structure, “anti-edges” and much more. -
181: Short-term Swing Trading Techniques – Ivanhoff
On this BST live show, Ivan from Ivanhoff Capital joins us to discuss “Short-term swing trading techniques”. Here are some of the topics you’ll discover in this episode:
The 2 most important factors in swing trading, Why short-term trading is better than longer-term trading, Measuring market environment and detecting changes, How to find the hot sectors in the market, Shorting stocks and adapting to market corrections and downtrends, Swing trading in choppy markets, Stacking the odds in your favour, High conviction setups, breakouts vs dips, protecting profits, trailing stops, time stops, swing trading psychology and much more. -
180: 3 ways traders kill trading strategies (and how to avoid them) – Rob Carver
On this BST live show, Rob Carver from systematicmoney.org joins us to discuss “3 ways traders kill trading strategies”. Here are just some of the topics you’ll discover in this episode:
What can go wrong with strategies in live trading, The impact of model complexity on prediction error, (and are simple models really better than more complicated ones - the answer may surprise you!), The dangers of overstated backtest performance, The impact of historic costs on backtest results, The 3 types of overfitting, including 1 that most traders don’t even realise they’re doing, and how to reduce their impact on your trading models, Strategy robustness and letting data define complexity, The overfitting "levels of sin", Correcting for the "multiple testing problem", Fitting metrics, sample size, data quality, prediction error and much more.
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