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# QuantLib Python - Discount Factor Interpolation within curve nodes


# QuantLib Python - Discount Factor Interpolation within curve nodes












Generated a discount curve, dCurve.PiecewiseLogLinearDiscount() using input par rate for terms (.5Y, 1Y, 2Y, 3Y, 5Y, 7Y, 10Y, 15Y, 20Y, 30Y) and output discount curve matching the input term structure. Any suggestions on how to output the discount factor curve on a .5Y interval term structure up to 30Y?

## Answer by Xiarpedia (score 1)

https://quant.stackexchange.com/a/77563

Based on your question I created the following curve example:

```
import QuantLib as ql
import pandas as pd

# Set the evaluation date
ql.Settings.instance().evaluationDate = ql.Date(30, 12, 2022)
# Declare a store variable for the helpers
helpers = []
# Create the attributes of the swap
calendar = ql.Sweden()
frequency = ql.Annual
convention = ql.ModifiedFollowing
# Synthetic market data
daycount = ql.Thirty360(ql.Thirty360.BondBasis)
index = ql.IborIndex(
    "MyIndex",
    ql.Period("3M"),
    2,
    ql.SEKCurrency(),
    calendar,
    ql.Following,
    False,
    daycount,
)

tenor = ["4Y", "5Y", "6Y", "7Y", "8Y", "9Y", "10Y", "12Y", "15Y", "20Y", "25Y", "30Y"]
quotes = [
    3.33,
    3.2775,
    3.235,
    3.205,
    3.1775,
    3.1525,
    3.1325,
    3.095,
    3.0275,
    2.92,
    2.815,
    2.6925,
]
for r, m in zip(quotes, tenor):
    rate = ql.QuoteHandle(ql.SimpleQuote(r / 100.0))
    tenor = ql.Period(m)
    helpers.append(
        ql.SwapRateHelper(
            rate, tenor, calendar, frequency, convention, daycount, index
        )
    )
curve = ql.PiecewiseLogLinearDiscount(0, calendar, helpers, ql.Actual365Fixed())
curve.enableExtrapolation()
dates, rates = zip(*curve.nodes())
```

Which leaves me at the stage you are at. We can then interpolate on a 5 year interval as following:

```
# Interpolate on a 5Y interval
nodes = []
start_date = ql.Date(30, 12, 2022)
while True:
    start_date += ql.Period("5Y")
    nodes.append(start_date)
    if start_date > ql.Date(30, 12, 2053):
        break
discount_factors = [curve.discount(d) for d in nodes]
display(pd.DataFrame(dict({"Date": nodes, "Discount Factor": discount_factors})))
```

Which results in:

|  | Date | Discount Factor |
| 0 | December 30th, 2027 | 0.851205 |
| 1 | December 30th, 2032 | 0.735589 |
| 2 | December 30th, 2037 | 0.641855 |
| 3 | December 30th, 2042 | 0.567462 |
| 4 | December 30th, 2047 | 0.508218 |
| 5 | December 30th, 2052 | 0.464726 |
| 6 | December 30th, 2057 | 0.424997 |

## Answer by Luigi Ballabio (score 0)

https://quant.stackexchange.com/a/77564

You can pass any date you like to the `discount` method of the curve you built. It will return the corresponding discount factor.