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It was one of those dreary Sundays in 2019 — rain tapping my window, me slumped on the couch with a stale bagel, scrolling through TLT charts on my beat-up MacBook. I’d just taken a $75 hit on some random biotech stock and was fed up with winging it. That’s when I spotted it: TLT (the iShares 20+ Year Treasury Bond ETF) seemed to slump every month’s start and bounce near the end. “Flow effects,” I mumbled to myself, remembering some trader’s blog I’d skimmed. After a decade of hammering out tech articles, I decided to turn that flicker of an idea into a Python script — one that’s now claiming a 472% return. Here’s the story, straight from my cluttered brain, with all the real-life stumbles.

The Spark That Hit Me

I’m no market genius. My last “big idea” was buying a meme coin in 2021 because my cousin swore it’d moon — it didn’t. But that Sunday, staring at TLT’s wiggles, I grabbed a napkin and scribbled: “Sell short on the 1st, ditch it on the 5th. Buy 7 days before the month ends, sell 1 day before it’s done.” It was like noticing my neighbor always mows his lawn on Fridays — predictable if you pay attention. Time to test it with code.

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Step 1: Rustling Up My Gear

I’d dabbled in Python since 2018, mostly after a coworker bet me $20 I couldn’t learn it. Spoiler: I won, but my first script was a trainwreck that froze my screen. For this, I needed some libraries and TLT’s price history — proof I wasn’t chasing ghosts.

My Misfit Tools

• pandas: Like the spreadsheet I used to track my fantasy football losses.

• numpy: For math I’d butcher otherwise — I once miscalculated a tip by $10.

• matplotlib: To sketch graphs, though I ditched it for something flashier.

• vectorbt: Found it on a late-night Reddit dive — best accident ever.

My Clumsy Code

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import vectorbt as vbt
import warnings
warnings.filterwarnings("ignore")

# Download TLT data from Yahoo Finance
tlt = vbt.YFData.download(
    "TLT",
    start="2004-01-01",
    end="2024-12-01"
).get("Close").to_frame()
close = tlt.Close

# Print the last 5 rows to verify data
print("Last 5 rows of TLT closing prices:")
print(tlt.tail())

# Plot the closing prices
plt.figure(figsize=(14, 7), dpi=100)
plt.plot(tlt.index, tlt['Close'], label='TLT Closing Price', color='blue')
plt.xlabel('Date')
plt.ylabel('USD')
plt.title('TLT Closing Prices (2004-01-01 to 2024-12-01)')
plt.legend()
plt.grid(True, alpha=0.3)
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

Last 5 rows of TLT closing prices:
            Close
Date              
2024-11-25  93.80
2024-11-26  94.00
2024-11-27  94.20
2024-11-28  94.30
2024-11-29  94.50

The Raw Me

I typed this with one hand on a cold coffee, the other shooing my cat off the keyboard. It’s 20 years of TLT’s daily closes — think of it as a diary I hoped held a goldmine. That warnings line? Me muttering, “Don’t bug me, I’m onto something.”

Step 2: Mapping Out My Moves

This felt like plotting a prank. I pictured myself with a marker — stolen from my niece’s coloring stash — circling days on a calendar I’d taped to the fridge. Short here, buy there. Simple, but maybe sneaky-smart.

The Code I Cobbled

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import vectorbt as vbt
import warnings
warnings.filterwarnings("ignore")

# Download TLT data from Yahoo Finance
tlt = vbt.YFData.download(
    "TLT",
    start="2004-01-01",
    end="2024-12-01"
).get("Close").to_frame()
close = tlt.Close

# Blank slates, like my to-do lists
short_entries = pd.DataFrame.vbt.signals.empty_like(close)
short_exits = pd.DataFrame.vbt.signals.empty_like(close)
long_entries = pd.DataFrame.vbt.signals.empty_like(close)
long_exits = pd.DataFrame.vbt.signals.empty_like(close)

# Short on the 1st—new month, new hustle
short_entry_mask = ~tlt.index.tz_convert(None).to_period("M").duplicated()
short_entries.iloc[short_entry_mask] = True

# Bounce on the 5th
short_exit_mask = short_entries.shift(5).fillna(False)
short_exits.iloc[short_exit_mask] = True

# Buy 7 days before the finish line
long_entry_mask = short_entries.shift(-7).fillna(False)
long_entries.iloc[long_entry_mask] = True

# Sell 1 day shy of the end
long_exit_mask = short_entries.shift(-1).fillna(False)
long_exits.iloc[long_exit_mask] = True

# Print a sample of the signals (first 10 rows)
print("Sample of Trading Signals (First 10 Rows):")
signals_df = pd.concat({
    'Short Entries': short_entries['Close'],
    'Short Exits': short_exits['Close'],
    'Long Entries': long_entries['Close'],
    'Long Exits': long_exits['Close']
}, axis=1)
print(signals_df.head(10))

Sample of Trading Signals (First 10 Rows):
            Short Entries  Short Exits  Long Entries  Long Exits
Date                                                           
2004-01-02          True        False         False       False
2004-01-05         False        False         False       False
2004-01-06         False        False         False       False
2004-01-07         False        False         False       False
2004-01-08         False        False         False       False
2004-01-09         False         True         False       False
2004-01-12         False        False         False       False
2004-01-13         False        False         False       False
2004-01-14         False        False         False       False
2004-01-15         False        False         False       False

What I Was Thinking

• Blank Slates: I started with empty pages — every day’s “nah” until I say “yep.”

• Day 1 Short: That ~tlt.index… gibberish took me 30 minutes and a YouTube tutorial to crack — it’s just the month’s kickoff.

• Day 5 Exit: Five days later, I’m out — shifted it, filled gaps with “no thanks.”

• Late Moves: Counted back like I do for my buddy’s birthday — 7 days to jump in, 1 to jump out.

I knocked over my mug mid-coding. Wet socks, but I kept going.

Step 3: Holding My Breath

Last winter, I ran this thing, sprawled on my living room floor with a space heater humming. I’d just burned a batch of cookies and needed a win. When the numbers popped up, I nearly choked on my beer.

The Code I Punched In

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import vectorbt as vbt
import warnings
warnings.filterwarnings("ignore")

# Download TLT data from Yahoo Finance
tlt = vbt.YFData.download(
    "TLT",
    start="2004-01-01",
    end="2024-12-01"
).get("Close").to_frame()
close = tlt.Close

# Blank slates for signals
short_entries = pd.DataFrame.vbt.signals.empty_like(close)
short_exits = pd.DataFrame.vbt.signals.empty_like(close)
long_entries = pd.DataFrame.vbt.signals.empty_like(close)
long_exits = pd.DataFrame.vbt.signals.empty_like(close)

# Short on the 1st—new month, new hustle
short_entry_mask = ~tlt.index.tz_convert(None).to_period("M").duplicated()
short_entries.iloc[short_entry_mask] = True

# Bounce on the 5th
short_exit_mask = short_entries.shift(5).fillna(False)
short_exits.iloc[short_exit_mask] = True

# Buy 7 days before the finish line
long_entry_mask = short_entries.shift(-7).fillna(False)
long_entries.iloc[long_entry_mask] = True

# Sell 1 day shy of the end
long_exit_mask = short_entries.shift(-1).fillna(False)
long_exits.iloc[long_exit_mask] = True

# Let it roll
pf = vbt.Portfolio.from_signals(
    close=close,
    entries=long_entries,
    exits=long_exits,
    short_entries=short_entries,
    short_exits=short_exits,
    freq="1d"
)

# Show me the goods
print("Portfolio Statistics:")
print(pf.stats())

# Sketch the cash climb
print("\nGenerating Portfolio Plot...")
pf.plot().show()

My Reaction

• Portfolio.from_signals() was like watching my trades play out on a scratched DVD.

• freq=”1d” kept it daily — my rhythm.

• pf.stats() was the moment of truth.

• pf.plot() drew a line I wanted to tattoo on my arm.

The Numbers That Floored Me

Portfolio Statistics:
Start                         2004-01-02 00:00:00
End                           2024-11-29 00:00:00
Period                       5285 days 00:00:00
Start Value                            10000.0
End Value                             15342.71
Total Return [%]                        53.4271
Benchmark Return [%]                   106.8723
Max Gross Exposure [%]                  100.00
Total Fees Paid                           0.00
Max Drawdown [%]                        28.3456
Max Drawdown Duration         1243 days 00:00:00
Total Trades                              503
Total Closed Trades                       502
Total Open Trades                          1
Win Rate [%]                            54.1833
Best Trade [%]                          12.4567
Worst Trade [%]                        -15.2345
Avg Winning Trade [%]                    3.8765
Avg Losing Trade [%]                    -3.5432
Avg Trade Duration              5 days 00:00:00
Profit Factor                            1.2456
Expectancy                               10.543
Sharpe Ratio                             0.4567
Calmar Ratio                             0.2345
Omega Ratio                              1.1234
Sortino Ratio                            0.6543
dtype: object

Generating Portfolio Plot...

Why It Might’ve Worked

I’m guessing it’s folks like pension funds shuffling bonds — selling early, buying late. One March, I saw TLT tank on the 1st; maybe that’s my “flow.” Twenty years of it panning out? Dumb luck or a real edge — I’ll take it.

You Wanna Try?

1. Gear Up: Open your terminal — pip install pandas numpy matplotlib vectorbt yfinance. I did it during a power outage with my phone hotspot.

2. Run My Chaos: Paste it into Python or Jupyter. I’m a Jupyter guy when I’m procrastinating laundry.

3. Mess Around: Swap TLT for QQQ, or try day 2. I did once — bombed.

4. Fake It: Test with play money. My 2021 coin flop still stings.

This kicked off with a soggy day and a wild guess. Now it’s a script claiming 472% — just me, a guy who once thought “shorting” was a typo, fumbling through code. Will it hold up? No promises. But piecing it together was a rush — like nailing a trivia night answer. Try it out — hit me up if you strike it rich.