# Backtesting Double Inside Bar Positional Strategy Using Python

## Prerequisite

In the last chapters,

So, We need to change the `check_high_breakout_and_save` function we designed in Index Inside Bar Positional Strategy Chapter. Then, We can just follow the rest of the codebase of Double Inside Bar Intraday Strategy Chapter as it is. So,

```				```
to_date = from_date + datetime.timedelta(hours=5)
```
```

becomes –

```				```
to_date=from_date+ datetime.timedelta(days=5)
```
```

You can download the rest of the `check_high_breakout_and_save` function from  Index Inside Bar Positional Strategy Chapter. Let’s not discuss the same thing over and over again complicating things –

## Plotting the Equity Curve

Since the remainder of the codebase and the process remain unchanged, let’s skip directly to the equity curve to examine if there are any anomalies or unusual patterns.

## Comparing the Performance Metrics with Intraday Strategy

While it may be intriguing to observe the evolution of performance metrics in the Positional Strategy’s results, a comprehensive analysis requires a side-by-side comparison with the Intraday Strategy’s data. Without this comparative context, it’s challenging to draw meaningful conclusions.

Therefore, let’s proceed to examine both strategies in parallel to gain a more insightful perspective.

```				```
Net of sell_pl_points: 1294312.709999999
Positive sell_pl_points count: 635
Negative sell_pl_points count: 551
Total sell_pl_points count: 2479
```
```
```				```
Average of sell_pl_points: 522.110814844695
Average of positive sell_pl_points: 5536.072881889762
Average of negative sell_pl_points: -4031.022813067151
```
```

## Double IB Positional

```				```
Net of sell_pl_points: 1359112.0599999984
Positive sell_pl_points count: 768
Negative sell_pl_points count: 993
Total sell_pl_points count: 2479
```
```
```				```