Lesson 5: Lists — Python for Algo Trading

LESSON 05 · SECTION 1

What is a List?

intermediate

A list is an ordered, mutable collection of items enclosed in square brackets. Items are accessed by index starting from 0. Lists can hold any type — prices, strings, other lists — and grow/shrink dynamically.

📋
Ordered
Items stay in insertion order. prices[0] is always the first price you added.

✏️

Mutable

You can add, remove, or change items after creation. Essential for live candle buffers.

🔢

Zero-indexed

list[0] = first, list[-1] = last. Negative indexes count from the end.

🌀

Mixed types

A list can hold integers, floats, strings, even other lists (2D arrays for OHLC).

list_basics.pypython

closes   = [22300, 22360, 22440, 22410, 22500]
watchlist = ["NIFTY", "BANKNIFTY", "RELIANCE", "TCS"]

print(closes[0])     # 22300 — first item
print(closes[-1])    # 22500 — last item
print(len(closes))   # 5 — number of items
print(max(closes))   # 22500 — highest close
print(min(closes))   # 22300 — lowest close
print(sum(closes))   # 112010 — sum

Output

Section 2

Slicing — Extract Candle Windows

Slicing extracts a sub-list using [start:stop:step]. In algo trading, slicing is how you extract the last N candles to calculate a moving average or detect patterns.

slicing.pypython

closes = [22100, 22200, 22300, 22380, 22440, 22410, 22500]

print(closes[2:5])    # [22300, 22380, 22440] — index 2 to 4
print(closes[-3:])    # [22440, 22410, 22500] — last 3 candles
print(closes[::2])    # every other candle

# 3-period Simple Moving Average on last 3 closes
last3 = closes[-3:]
sma3  = sum(last3) / len(last3)
print(f"SMA(3) = {sma3:.1f}")

# 5-period SMA
last5 = closes[-5:]
sma5  = sum(last5) / len(last5)
print(f"SMA(5) = {sma5:.1f}")

Output

[22300, 22380, 22440]
[22440, 22410, 22500]
[22100, 22300, 22440, 22500]
SMA(3) = 22450.0
SMA(5) = 22406.0

Section 3

List Methods for Trading

Method	What it does	Trading use
`append(x)`	Add item to end	Append new tick/candle to buffer
`pop()`	Remove & return last item	Remove oldest candle (FIFO buffer)
`remove(x)`	Remove first match	Remove exited stock from watchlist
`sort()`	Sort in place	Sort stocks by P&L
`reverse()`	Reverse in place	Flip candle order oldest→newest
`count(x)`	Count occurrences	Count BUY signals in history
`index(x)`	Find position	Find where a price level was last seen

list_methods.pypython

# Rolling 5-candle buffer
candle_buffer = [22100, 22200, 22300, 22380, 22440]
new_candle    = 22500

# Add new, remove oldest
candle_buffer.append(new_candle)
candle_buffer.pop(0)
print("Buffer:", candle_buffer)

# Signal history
signals = ["BUY", "HOLD", "BUY", "SELL", "BUY", "HOLD"]
print("BUY signals:", signals.count("BUY"))

# Watchlist management
watchlist = ["NIFTY", "BANKNIFTY", "RELIANCE"]
watchlist.append("TCS")
watchlist.remove("BANKNIFTY")
print("Watchlist:", watchlist)

Output

Buffer: [22200, 22300, 22380, 22440, 22500]
BUY signals: 3
Watchlist: ['NIFTY', 'RELIANCE', 'TCS']

Section 4

2D Lists — OHLC Candle Arrays

A 2D list is a list of lists. This is the natural structure for OHLC candle data — each inner list is one candle with [open, high, low, close, volume].

ohlc_2d.pypython

# NIFTY 5-min OHLCV — [open, high, low, close, volume]
ohlcv = [
    [22300, 22380, 22270, 22360, 125000],
    [22360, 22400, 22320, 22340, 98000],
    [22340, 22450, 22330, 22440, 143000],
    [22440, 22480, 22390, 22410, 112000],
    [22410, 22510, 22400, 22500, 165000],
]

# Extract all close prices
closes  = [c[3] for c in ohlcv]
highs   = [c[1] for c in ohlcv]
volumes = [c[4] for c in ohlcv]

print(f"Closes      : {closes}")
print(f"Session High: ₹{max(highs)}")
print(f"Avg Volume  : {sum(volumes)//len(volumes):,}")

# Latest candle
latest = ohlcv[-1]
print(f"\nLatest Candle:")
print(f"  O={latest[0]} H={latest[1]} L={latest[2]} C={latest[3]}")

Output

Closes      : [22360, 22340, 22440, 22410, 22500]
Session High: ₹22510
Avg Volume  : 128600

Latest Candle:
  O=22410 H=22510 L=22400 C=22500

Section 5

SMA Calculator with Lists

Using list slicing, we can compute a rolling Simple Moving Average — the foundation of dozens of trading strategies.

sma_calculator.pypython

def sma(prices, period):
    """Simple Moving Average over last N periods"""
    if len(prices) < period:
        return None
    window = prices[-period:]
    return round(sum(window) / period, 2)

closes = [22100, 22200, 22300, 22380, 22440,
          22410, 22500, 22480, 22520, 22560]

sma5  = sma(closes, 5)
sma10 = sma(closes, 10)
ltp   = closes[-1]

print(f"LTP   : ₹{ltp}")
print(f"SMA5  : ₹{sma5}")
print(f"SMA10 : ₹{sma10}")

if sma5 > sma10:
    print("📈 SMA5 > SMA10 — Bullish crossover")
else:
    print("📉 SMA5 < SMA10 — Bearish crossover")

Output

LTP   : ₹22560
SMA5  : ₹22494.0
SMA10 : ₹22389.0
📈 SMA5 > SMA10 — Bullish crossover

💡 PRO TIP

In production systems, you'd use pandas or numpy for SMA calculations on large datasets. But understanding the list-based version first makes you a better trader-developer — you know what's happening under the hood.

Section 6

Quick Quiz

QUESTION 1 OF 3

Given closes = [22100, 22200, 22300], what does closes[-1] return?

A22100

B22200

C22300

DIndexError

QUESTION 2 OF 3

What does closes[-3:] return from a 7-item list?

AThe first 3 items

BThe last 3 items

CItems at index 3, 4, 5

DAn error

QUESTION 3 OF 3

Which method correctly implements a rolling FIFO buffer (add new, remove oldest)?

Abuf.add(x); buf.remove(x)

Bbuf.append(x); buf.pop(0)

Cbuf.push(x); buf.shift()

Dbuf.insert(0, x); buf.pop()

Section 7

Practice Exercises

EXERCISE 1 · EASY

Session Statistics

Given closes = [22300, 22360, 22440, 22410, 22500, 22480, 22520], print the session high, low, average, and range (high - low) using list functions.

Hint: use max(), min(), sum()/len().

EXERCISE 2 · MEDIUM

Rolling 3-Period SMA

Write a function that takes a list of closes and a period, and returns a new list of SMA values. For candles before the period, append None. Test with a 10-candle close list and period=3.

Hint: use slicing closes[i-period+1:i+1] inside a for loop.

EXERCISE 3 · CHALLENGE

Trade History Manager

Maintain a list of trade dicts [{sym, side, entry, exit, pnl}]. Write functions to: (1) add a trade, (2) get total P&L, (3) get the best and worst trade by P&L.

Hint: use max(trades, key=lambda t: t['pnl']) for best trade.

🚀 UP NEXT — LESSON 6

Lists store ordered sequences. But what if you want to access trade data by name (e.g., trade["symbol"]) instead of index? Lesson 6: Dictionaries — the data structure that powers trade objects, config files, and API responses.

Phase 1 · Lesson 5 of 9

Lists

Lists — Ordered Collections