Converted Date Column to Datetime Index and Displayed Column Names

Jul 11, 2024, 4:37 PM

name df data make Date a datetime index and show me column names

The data has been successfully loaded and the 'Date' column has been set as the datetime index. Here are the column names in the dataset:

cad_ig_er_index
cad_ig_sprds
vix
us_hy_er_index
us_ig_er_index
spx_index

Feel free to ask any further questions or request specific analyses or visualizations.

Act like an expert in vector bt…back test the following strategies:

buy/sell 'cad_ig_er_index if last value for cad_ig_er_index is above/below it’s 50 period MA
buy/sell 'cad_ig_er_index if last value for us_ig_er_index is above/below it’s 50 period MA
buy/sell 'cad_ig_er_index if last value for us_hy_er_index is above/below it’s 50 period MA
buy/sell 'cad_ig_er_index if last value for spx is above/below it’s 50 period MA

The backtesting results for the strategies are as follows:

Strategy based on cad_ig_er_index 50-period MA
- Cumulative Return: 1.375431
- Cumulative Strategy Return: 1.907888
Strategy based on us_ig_er_index 50-period MA
- Cumulative Return: 1.375431
- Cumulative Strategy Return: 3.112625
Strategy based on us_hy_er_index 50-period MA
- Cumulative Return: 1.375431
- Cumulative Strategy Return: 3.112179
Strategy based on spx_index 50-period MA
- Cumulative Return: 1.375431
- Cumulative Strategy Return: 2.732799

Would you like to visualize these results or perform any further analysis?

do it with vector bt

VectorBT is a powerful backtesting library that allows for efficient and flexible backtesting of trading strategies. Let's implement the strategies using VectorBT and visualize the results.

First, we need to install the VectorBT library.

The strategies have been successfully backtested using VectorBT. Here are the results:

Strategy based on cad_ig_er_index 50-period MA
Strategy based on us_ig_er_index 50-period MA
Strategy based on us_hy_er_index 50-period MA
Strategy based on spx_index 50-period MA

Let's visualize the cumulative returns for each strategy.

pls also add the buy and hold version of cad_ig_er_index to this last chart/analsysi and all analysis going forward

The cumulative returns for the strategies, including the buy and hold strategy for cad_ig_er_index, have been visualized. The chart now includes:

Strategy based on cad_ig_er_index 50-period MA
Strategy based on us_ig_er_index 50-period MA
Strategy based on us_hy_er_index 50-period MA
Strategy based on spx_index 50-period MA
Buy and hold strategy for cad_ig_er_index

Feel free to ask any further questions or request additional analyses.

Show performance metrics for each strategy

It appears that the frequency of the data needs to be explicitly set for the performance metrics calculations. Let's define the frequency globally and then calculate the performance metrics again.

Here are the performance metrics for each strategy:

Strategy	Total Return	Annualized Return	Annualized Volatility	Sharpe Ratio	Max Drawdown
cad_ig_er_index	0.630653	0.029186	0.028213	1.033633	-0.037619
us_ig_er_index	1.053175	0.043232	0.030857	1.386994	-0.047829
us_hy_er_index	1.081278	0.044067	0.029449	1.478945	-0.049365
spx_index	0.947962	0.040009	0.031768	1.250705	-0.061669
buy_and_hold	0.375924	0.018952	0.039354	0.496758	-0.208533

Feel free to ask any further questions or request additional analyses.

Continue the analysis

Let's continue the analysis by examining additional performance metrics and visualizing them.

We'll look at:

Rolling Sharpe Ratio
Rolling Volatility
Drawdown

Let's start by calculating and visualizing the rolling Sharpe Ratio for each strategy.

It seems that the Rolling object does not have a sharpe_ratio method. Instead, we need to calculate the rolling mean and standard deviation of returns, and then compute the rolling Sharpe Ratio manually.

Let's proceed with this approach.

The rolling Sharpe Ratio (1 year) for each strategy has been visualized. This provides insight into the risk-adjusted performance of each strategy over time.

Next, let's calculate and visualize the rolling volatility for each strategy.

The rolling volatility (1 year) for each strategy has been visualized. This provides insight into the variability of returns for each strategy over time.

Next, let's calculate and visualize the drawdown for each strategy.

The analysis has been extended to include additional performance metrics and visualizations for each strategy. Here are the steps taken:

Rolling Sharpe Ratio (1 Year): Calculated and visualized the rolling Sharpe Ratio for each strategy to provide insight into the risk-adjusted performance over time.
Rolling Volatility (1 Year): Calculated and visualized the rolling volatility for each strategy to show the variability of returns over time.
Drawdown: Calculated and visualized the drawdown for each strategy to illustrate the peak-to-trough decline during a specific period.

Feel free to ask any further questions or request additional analyses.

Continue the analysis

Let's continue the analysis by calculating and visualizing the cumulative returns for each strategy. This will help us understand the overall performance of each strategy over the entire period.

The cumulative returns for each strategy have been calculated and visualized. This provides insight into the overall performance of each strategy over the entire period.

Here is a summary of the analyses performed:

Rolling Sharpe Ratio (1 Year): Calculated and visualized the rolling Sharpe Ratio for each strategy to provide insight into the risk-adjusted performance over time.
Rolling Volatility (1 Year): Calculated and visualized the rolling volatility for each strategy to show the variability of returns over time.
Drawdown: Calculated and visualized the drawdown for each strategy to illustrate the peak-to-trough decline during a specific period.
Cumulative Returns: Calculated and visualized the cumulative returns for each strategy to understand the overall performance over the entire period.

Feel free to ask any further questions or request additional analyses.

optimize each parameter to get the best backtest possible (test 10-75 MA)

To optimize the parameters for the best backtest possible, we will perform a grid search over the moving average (MA) periods ranging from 10 to 75. This involves testing each combination of parameters and selecting the one that yields the best performance based on a chosen metric, such as the Sharpe Ratio or cumulative returns.

Let's proceed with the optimization.

The optimization process has been completed. The best parameters for the moving average (MA) crossover strategy are:

Short MA Period: 10
Long MA Period: 11

These parameters yield the highest Sharpe Ratio.

Would you like to proceed with a detailed backtest using these optimized parameters or perform any other analysis?

Run a detailed backtest with these optimized parameters

The detailed backtest using the optimized parameters (Short MA Period: 10, Long MA Period: 11) has been completed. Here are the performance metrics:

Total Return: 0.0
Annualized Return: 0.0
Annualized Volatility: 0.0
Sharpe Ratio: ∞ (infinite)
Max Drawdown: 0.0

It appears that the strategy did not generate any trades, resulting in zero returns and volatility. This could be due to the close proximity of the short and long MA periods. Would you like to adjust the parameters or perform any other analysis?

lets go back to original strategy before optimization........can we improve these results if use some sort of market regime detection model (hidden markov models? others?)

Sure, we can explore using a market regime detection model to potentially improve the results. Hidden Markov Models (HMM) are a popular choice for this purpose. The idea is to detect different market regimes (e.g., bull, bear, sideways) and adjust the strategy accordingly.

Let's proceed with the following steps:

Train an HMM: Train a Hidden Markov Model on the price data to detect different market regimes.
Segment the Data: Use the trained HMM to segment the data into different regimes.
Backtest in Each Regime: Run the original strategy in each detected regime and analyze the performance.

Let's start by training the HMM on the price data.

It seems there is an issue with indexing when trying to add the hidden states to the price DataFrame. Let's fix this by ensuring the indices match correctly.

The Hidden Markov Model (HMM) has been trained and used to detect different market regimes. The detected regimes have been plotted on the price data.

Next, we can proceed with running the original strategy in each detected regime and analyze the performance. This will help us understand if the strategy performs better in certain market regimes.

Would you like to proceed with this analysis?

Can you run the original strategy in each regime?

The original strategy was run in each detected market regime. Here are the performance metrics for each regime:

Regime 0:
- Total Return: 0.0
- Annualized Return: 0.0
- Annualized Volatility: 0.0
- Sharpe Ratio: ∞ (infinite)
- Max Drawdown: 0.0
Regime 1:
- Total Return: 0.0
- Annualized Return: 0.0
- Annualized Volatility: 0.0
- Sharpe Ratio: ∞ (infinite)
- Max Drawdown: 0.0
Regime 2:
- Total Return: 0.0
- Annualized Return: 0.0
- Annualized Volatility: 0.0
- Sharpe Ratio: ∞ (infinite)
- Max Drawdown: 0.0

It appears that the strategy did not generate any trades in any of the regimes, resulting in zero returns and volatility. This could be due to the parameters of the moving averages or the nature of the detected regimes.

Would you like to adjust the parameters or explore a different approach?

Get started with Vizly