Even though the use of Fibonacci is widely applied, there is little research about how a structured risk management could improve such trading strategies, especially in leveraged markets. There are no studies which provide traders with an empirical tested framework on how to combine different risk overlays to achieve a superior performance with a Fibonacci-based trading strategy. The goal of this thesis is therefore to address this research gap in collaboration with Waller Financial Consulting.

For this purpose, daily price information for the S&P 500 E-mini futures market has been used to backtest a Fibonacci-based strategy with/without risk-management overlays. The risk-management tools used in this study are different stop-loss strategies (fixed, trailing, and ATR-based), position sizing techniques (fixed, Kelly, ATR), and a volatility filter. In addition, transaction costs and slippage had been incorporated to simulate real-life trading conditions as closely as possible. The results were then compared using different performance metrics such as Sharpe ratio or maximum drawdown.

The backtesting results of this thesis show, that the baseline strategy outperformed all tested risk-management strategies. It achieved the highest annualised return and the highest Sharpe ratio. Only one strategy produced a lower drawdown while all other strategies had a higher maximum drawdown than the baseline strategy, but this also came at a cost of a lower risk-adjusted return. The reason for the inferior performance was that most of the risk-overlays missed out on profitable recovery phases after drawdowns. Furthermore, statistical tests confirmed that no overlay delivered a mean trade return significantly different from the baseline strategy. In summary, the findings indicate that in this context the chosen risk-management overlays did not produce improved performance and did not mitigate the risk involved in futures trading.