Screening Stocks for Threshold Price Moves Within a Rolling Window

# How can I find stocks that have had a X% price swing within Y days, sorted by recency of said swing?


# How can I find stocks that have had a X% price swing within Y days, sorted by recency of said swing?












Let's say that I want to find stocks that have moved +-20% within a 10 day period.

`ABC` would match if at `t`, `ABC` is \$1 and at `t+8`, `ABC` is \$1.20.

`XYZ` would not match if at `t`, `XYZ` is \$1 and at `t+10`, `XYZ` is \$1.10, because it did not move 20% within 10 days.

I believe that I'm looking for some kind of volatility indicator that will also allow for a time range.

How can I also sort by how recently this indicator occurred?

Thanks for any help.

## Answer by Sergey Bushmanov (score 1)

https://quant.stackexchange.com/a/22742

As soon as you're comfortable with Python, let's do this exercise in three steps:

- Download data and calculate cumulative returns (or value of your position as if you invested $1 in each of the stocks)

- Define function that will capture stock movements in excess of predefined threshold, 10% in this case. This function is going to be the "indicator" you asked for.

- Plot "indicator" on top of the stock data.

Step 1.

```
# Imports  
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pandas_datareader import data
%matplotlib inline
plt.rcParams['figure.figsize']=(12,5)

# Definitions of global variables
N = 10
change = .1
stocks = ['GOOG','YHOO','AAPL','IBM','MSFT']

# Download of prices
price = pd.DataFrame()
for st in stocks:
    price[st] = data.DataReader(st, 'yahoo')['Adj Close']

# Calculation of cumulative returns
return_cum = (1+price.pct_change()).cumprod()
return_cum.plot();
```

Step 2.

Now, we need to define the indicator itself that will capture any movements, positive or negative, in excess of predefined `change`. The idea here is that I roll window `N` times, each time decreasing it by 1, and put at the right end of the window 1 if absolute cumulative change is more than change required. Finally, if for any window of changing length I have 1 on a certain day, the indicator returns SINGLE `1` for that day.

```
def change_within_NDays(x,N):
    ret = pd.DataFrame()
    for days in range(N+1):
        ret[days] = pd.rolling_apply(x, days, func=lambda x: abs((x[-1]/x[0]-1)) > change)
    return ret.any(1)

indicator = return_cum.apply(lambda x: change_within_NDays(x,N))
```

Step 3.

Finally, we can visualize the indicator by drawing it over stock prices:

```
_, ax = plt.subplots()
return_cum.plot(ax=ax)
indicator.plot(ax=ax, legend=0);
```

## Answer by Dr_Be (score 0)

https://quant.stackexchange.com/a/22727

You're not looking for volatility, at least not that measure alone. Imagine two stocks A and B, A is gaining a constant 2% every day whereas B is gaining 1% one day and -1% the other day. Then look at the return over the 10 days and the volatility of the two stocks.