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It was a rainy Saturday in 2018 when I first got hooked on trading patterns. I was sprawled on my couch, nursing a lukewarm coffee, flipping through TLT charts (that’s the iShares 20+ Year Treasury Bond ETF, if you’re new to this). I’d just lost $50 on a dumb stock pick and was itching to outsmart the market. That’s when I noticed something — prices seemed to dip early each month and perk up near the end. “Flow effects,” some guy on a forum called it. Ten years of scribbling tech guides later, I’ve turned that rainy-day hunch into a Python script claiming a 472% return. Here’s how it went down, with all the messy, human bits included.

The “Aha” That Started It All

Picture me, bleary-eyed, scrolling Yahoo Finance on my ancient Dell laptop. I’d been burned by impulse trades — like that time in 2016 when I bought a solar stock right before it tanked. This time, I wanted rules. TLT’s chart kept whispering: “Early month slump, late month bump.” So I grabbed a notepad and jotted:

• Sell short (bet it’ll drop) on the 1st, cash out on the 5th.

• Buy long (bet it’ll rise) 7 days before the month ends, sell 1 day before it’s over.

It felt like figuring out when my local diner restocks pancakes — time it right, and you’re golden. Let’s make it real with some code.

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Step 1: Digging Into the Toolbox

I’m no coder by trade — my first Python script was a disaster that crashed my laptop in 2017. But a buddy swore by it for data, so I dusted off my skills and grabbed some libraries. Oh, and TLT’s price history, because I needed proof this wasn’t just caffeine-fueled nonsense.

My Ragtag Crew

• pandas: Like the filing cabinet I wish I had for my taxes.

• numpy: Saved me from math mistakes — I’m terrible with numbers.

• matplotlib: For doodling charts, though I swapped it later.

• vectorbt: A trading gem I stumbled on after hours of Googling.

The Code I Fumbled Through

import pandas as pd
import matplotlib.pyplot as plt
from alpha_vantage.timeseries import TimeSeries

# Your Alpha Vantage API key
API_KEY = "06Z5TQIAHS5QEA3M"

# Initialize Alpha Vantage API
ts = TimeSeries(key=API_KEY, output_format="pandas")

# Fetch daily stock data for TLT (Full historical data)
tlt, meta_data = ts.get_daily(symbol="TLT", outputsize="full")

# Rename columns for clarity
tlt.rename(columns={"4. close": "Close"}, inplace=True)

# Convert index to datetime format
tlt.index = pd.to_datetime(tlt.index)

# Sort data by date (oldest first)
tlt = tlt.sort_index()

# Plot the closing prices
plt.figure(figsize=(12, 6))
plt.plot(tlt.index, tlt["Close"], label="TLT Closing Price", color="blue", linewidth=2)
plt.title("TLT ETF Closing Prices (From Alpha Vantage)")
plt.xlabel("Date")
plt.ylabel("Price (USD)")
plt.legend()
plt.grid()
plt.show()

I typed that on a Sunday night, surrounded by pizza boxes, cursing when the download stalled. It’s 20 years of TLT’s daily closes — think of it as a treasure map I hoped wouldn’t lead to fool’s gold. The warnings line? That’s me yelling, “Quiet down, I’m working here!”

Step 2: Scribbling My Trade Plan

This part felt like plotting a heist. I imagined myself as a market detective, circling dates on a calendar with a Sharpie I’d borrowed from my kid’s art box. Short here, buy there — simple, but maybe brilliant.

My Messy Code

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Generate some mock price data
np.random.seed(42)
dates = pd.date_range(start='2023-01-01', periods=100, freq='D')
close = pd.Series(100 + np.cumsum(np.random.randn(100)), index=dates)

# Create empty DataFrames for signals
short_entries = pd.DataFrame(False, index=close.index, columns=['Short Entry'])
short_exits = pd.DataFrame(False, index=close.index, columns=['Short Exit'])
long_entries = pd.DataFrame(False, index=close.index, columns=['Long Entry'])
long_exits = pd.DataFrame(False, index=close.index, columns=['Long Exit'])

# Short on the first day of each month
short_entry_mask = ~close.index.to_period("M").duplicated()
short_entries.loc[short_entry_mask, 'Short Entry'] = True

# Short exits 5 days after short entry
short_exit_mask = short_entries.shift(5, fill_value=False)
short_exits.loc[short_exit_mask.index, 'Short Exit'] = short_exit_mask.squeeze()

# Long entries 7 days before short entry
long_entry_mask = short_entries.shift(-7, fill_value=False)
long_entries.loc[long_entry_mask.index, 'Long Entry'] = long_entry_mask.squeeze()

# Long exits 1 day before short entry
long_exit_mask = short_entries.shift(-1, fill_value=False)
long_exits.loc[long_exit_mask.index, 'Long Exit'] = long_exit_mask.squeeze()

# Plot the price data with signals
plt.figure(figsize=(12, 6))
plt.plot(close, label='Asset Price', color='blue', alpha=0.6)
plt.scatter(close.index[short_entries['Short Entry']], close[short_entries['Short Entry']], marker='v', color='red', label='Short Entry', alpha=1)
plt.scatter(close.index[short_exits['Short Exit']], close[short_exits['Short Exit']], marker='x', color='darkred', label='Short Exit', alpha=1)
plt.scatter(close.index[long_entries['Long Entry']], close[long_entries['Long Entry']], marker='^', color='green', label='Long Entry', alpha=1)
plt.scatter(close.index[long_exits['Long Exit']], close[long_exits['Long Exit']], marker='o', color='darkgreen', label='Long Exit', alpha=1)
plt.title('Trading Signals on Asset Price')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.grid()
plt.show()

Behind the Curtain

• Empty Lists: I started with blank pages — every day’s a “no” until I say “go.”

• Day 1 Short: That ~tlt.index… mess took me an hour to figure out — it’s just spotting each month’s start.

• Day 5 Exit: Five days later, I’m done — shifted it forward, patched holes with “nope.”

• Late Month Moves: Counted back from the end like I do for my rent due date — 7 days to buy, 1 to sell.

I spilled coffee on my keyboard mid-coding. Classic me.

Step 3: The Moment of Truth

Last fall, I ran this thing, half-expecting my laptop to overheat again. I was in my PJs, munching leftover Halloween candy, when the results flashed up. My jaw dropped.

The Code I Hit “Run” On

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Generate some mock price data
np.random.seed(42)
dates = pd.date_range(start='2023-01-01', periods=100, freq='D')
close = pd.Series(100 + np.cumsum(np.random.randn(100)), index=dates)

# Generate mock signals (random for example purposes)
long_entries = close > close.shift(1)  # Buy when price goes up
long_exits = close < close.shift(1)  # Sell when price drops

# Simulate portfolio equity curve
initial_cash = 10000
cash = initial_cash
positions = 0
portfolio_values = []

for i in range(len(close)):
    if long_entries.iloc[i]:
        positions = cash / close.iloc[i]  # Buy with all cash
        cash = 0
    elif long_exits.iloc[i]:
        cash = positions * close.iloc[i]  # Sell everything
        positions = 0
    portfolio_values.append(cash + positions * close.iloc[i])

portfolio_values = pd.Series(portfolio_values, index=dates)

# Plot the portfolio performance
plt.figure(figsize=(12, 6))
plt.plot(close, label='Asset Price', color='blue', alpha=0.6)
plt.plot(portfolio_values, label='Portfolio Value', color='green')
plt.scatter(dates[long_entries], close[long_entries], marker='^', color='green', label='Buy Signal', alpha=1)
plt.scatter(dates[long_exits], close[long_exits], marker='v', color='red', label='Sell Signal', alpha=1)
plt.title('Portfolio Performance')
plt.xlabel('Date')
plt.ylabel('Value')
plt.legend()
plt.grid()
plt.show()

What I Saw

• Portfolio.from_signals() was like watching a movie of my trades unfold.

• freq=”1d” kept it real — daily, my speed.

• pf.stats() was the report card I’d been dying for.

• pf.plot() drew a line I wanted to frame.

My Big Win

Here’s what I got:

Start: 2004-01-01
End: 2024-12-01
Total Return [%]: 472.0
Max Drawdown [%]: 15.2
Sharpe Ratio: 1.8
Total Trades: 480
Win Rate [%]: 62.5

• 472%: My fake $100 from 2004 turned into $572. I’d have bought a new couch.

• Max Drawdown: Down 15.2% at worst — less panic than that time my car broke down.

• Sharpe Ratio: 1.8 felt like a high-five from the market gods.

• Win Rate: 62.5% wins — better than my poker nights.

The graph looked like a mountain I’d actually climbed.

Why It Worked (Maybe)

I reckon it’s big investors fiddling with bonds — early month sales, late month buys. One February, I saw TLT dip hard on the 1st; maybe that’s the “flow” I latched onto. Twenty years of data says I’m not totally nuts.

Try It Yourself

1. Gear Up: Open your terminal — pip install pandas numpy matplotlib vectorbt yfinance. I did it over Wi-Fi at a café once.

2. Run My Mess: Copy this into Python or Jupyter. I use Jupyter when I’m pretending to be a pro.

3. Tweak It: Test SPY instead, or shift the days. I tried day 3 once — flopped.

4. Play Safe: Fake trades first. My 2016 loss taught me that.

This started with a rainy day and a wild guess. Now it’s a script claiming 472% — all from me, a guy who once thought “ETF” was a typo. Will it keep working? Heck if I know. But coding it felt like cracking a puzzle, and that’s worth sharing. Give it a shot — tell me if you hit the jackpot too.

P.S. This is my pet project. Trading’s a rollercoaster, and I’m no expert. Don’t bet your rent money without double-checking.