How Much Python Do You Need to Build a Trading Strategy?

You can start once you can load time-stamped prices, compute a return series, write a rule that produces positions, and test the rule with correct timing and transaction costs. Basic functions, boolean conditions, and pandas indexing are enough for that first project. The most important skill is being able to trace which information each line of code uses.

The smallest useful backtest

Learn enough Python to complete one reproducible loop.

Read a CSV with parsed dates, sort the rows, and decide how to handle duplicates and missing observations.
Compute arithmetic returns and a rolling indicator.
Convert the indicator into a target position such as zero or one.
Specify when the target can be acted on. Align the held position with the return interval it earns.
Charge a cost when the position changes, compare with a simple benchmark, and reserve a later time period for evaluation.

Start with a few observations you can check manually. Use functions to separate loading, signal generation, and evaluation once the calculations agree with your hand check.

Assume a signal becomes available at each close. For a simplified test, model execution at the following close. A signal first turning on at Monday's close leads to entry at Tuesday's close, so the first full close-to-close return it can earn runs from Tuesday to Wednesday. Friday's P&L can be checked by locating Thursday's executed position, Friday's return, and any new trade cost on Friday.

A 10 basis point one-way cost means a zero-to-one position change costs

0.001

in return units. A flat day with no position change incurs zero cost in this simple model. Use that distinction to find implementations that subtract costs from every invested day.

You should be able to point at one row and say which position was already held, what price movement it earned, and which fee was charged on that row. Do that manually before vectorising the calculation. For a long-or-cash strategy, three columns for signal, executed position and held position are usually clearer than a clever expression with multiple shifts. Add a fourth column for absolute position changes. A no-trade test should leave that column and total costs at zero. Those checks will take you further than learning advanced Python syntax before building anything.

A minimal signal with delayed execution

import pandas as pd

prices = pd.read_csv("prices.csv", parse_dates=["date"]).set_index("date")
close = prices.sort_index()["close"].dropna()
returns = close.pct_change(fill_method=None)
average = close.rolling(20, min_periods=20).mean()
target = (close > average).astype(float)
executed = target.shift(1, fill_value=0)
held = executed.shift(1, fill_value=0)
turnover = executed.diff().abs().fillna(0)
net = held * returns - 0.001 * turnover
print(pd.DataFrame({"target": target, "held": held, "net": net}).tail())

Once the delayed-execution version reconciles, use an existing Strategy Lab backtest to compare how a larger backtest reports positions and performance.