lutrx / project-trading-analyisis

Trading strategy analysis | Mid-bootcamp project

Lisa-Maria Bieker Ironhack Data Analysis

Topic

The aim of this project was to disprove the Random-Walk-Theory. Therefore, three trading strategies were checked with different forex trading pairs (daily and minute data).

The following strategies were tested:

1. Red green candle
2. Reversed candle
3. Moving average crossover

Trading pairs: USDJPY, EURUSD, GBPUSD, USDCAD

Presentation of the project was done with Google slides: link

The following tools/skills were used:

• Jupyter Notebook
• Python: Pandas, Numpy, Seaborn, Matplotlib
• MySQL
• Tableau
• Statistical analysis

Dataset

Data was used from these sources:

• Daily data
• Daily and minute data --> account needed to get data

All datasets contained the following columns:

• Date/time
• Open
• High
• Low
• Close
• Volume

Preparation of data and testing of strategies

• dropping of not required columns (volume etc.)
• conversion of data in ticks
• calculation of difference open - close prices per row
• calculation of classification and order type columns (long, short, none) depending on strategy per row
• calculation of profit per row
• preparation for figures: calculation of cumulative profit per row

Analysis and visualization

• calculation of how many standard deviations result of strategy is away from expected profit value of 0 --> significance level for accepting strategy as valid is 3.9 standard deviations
• analysis of number of trades vs cumulative profit: Tableau and Matplotlib

Conclusion

Adapted red green strategy in combination with USDJPY minute data resulted in a profitable strategy with a standard deviation of 5.22. Under the given significance level and by usage of this strategy and dataset the Random-Walk-Theory could be disproved.