Fija vault analysis

• Run erc-4626-single-vault.ipynb analysis for all Fija

To generate a a HTML report:

jupyter nbconvert --no-input --to html docs/source/tutorials/erc-4626-fija-vaults.ipynb
mv  docs/source/tutorials/erc-4626-fija-vaults.html ~/Downloads

Setup

• Set up notebook rendering output mode

• Use static image charts so this notebook is readeable on Github / ReadTheDocs

import pandas as pd
from plotly.offline import init_notebook_mode
import plotly.io as pio

from eth_defi.vault.base import VaultSpec
from eth_defi.research.notebook import set_large_plotly_chart_font

pd.options.display.float_format = "{:,.2f}".format
pd.options.display.max_columns = None
pd.options.display.max_rows = None


# Set up Plotly chart output as SVG
image_format = "png"
width = 1400
height = 800

# https://stackoverflow.com/a/52956402/315168
init_notebook_mode()

# https://plotly.com/python/renderers/#overriding-the-default-renderer
pio.renderers.default = image_format

current_renderer = pio.renderers[image_format]
# Have SVGs default pixel with
current_renderer.width = width
current_renderer.height = height

# Set all Plotly charts to use large font sizes for better readability,
# for sharing on mobile
set_large_plotly_chart_font(
    line_width=5,
    legend_font_size=16,
    axis_title_font_size=16,
    font_size=20,
)
pio.templates.default = "custom"

print("Notebook formatting setup done")

Notebook formatting setup done

Read and clean raw scanned vault price data

• Read the Parquet file produced earlier with price scan

• Clean the data if necessary

import pickle
from pathlib import Path

from eth_defi.vault.vaultdb import VaultDatabase

data_folder = Path("~/.tradingstrategy/vaults").expanduser()
vault_db_path = data_folder / "vault-db.pickle"

vault_db: VaultDatabase = pickle.load(open(vault_db_path, "rb"))

cleaned_data_parquet_file = data_folder / "cleaned-vault-prices-1h.parquet"
prices_df = pd.read_parquet(cleaned_data_parquet_file)

print(f"We have {len(vault_db):,} vaults in the database and {len(prices_df):,} price rows.")

We have 10,357 vaults in the database and 8,393,611 price rows.

Choose vaults to examine

• We pick Fija vaults by their name

from eth_defi.vault.base import VaultSpec
from eth_defi.vault.vaultdb import VaultLead
from eth_defi.research.vault_metrics import format_vault_database

def _lower_name(v: VaultLead):
    name = v["Name"]
    if name:
        return name.lower()
    return ""


# Map out vaults we are going to examine in this notebook
leads = {spec: vault for spec, vault in vault_db.items() if "fija" in _lower_name(vault)}

vault_df = format_vault_database(leads, index=False)
vault_df = vault_df.set_index("Name")
vault_df = vault_df.sort_values("NAV", ascending=False)

vault_df = vault_df[vault_df["Deposit count"] > 40]
display(vault_df)

	Symbol	Address	Denomination	NAV	Protocol	Mgmt fee	Perf fee	Shares	First seen	_detection_data	_denomination_token	_share_token	Chain	Protocol identified	Stablecoin denominated	ERC-7540	ERC-7575	Fee detected	Deposit count	Redeem count	Total events	Age
Name
fija Strategy USDC GMXv2	sUSDC_GMXv2	0xadbb6ec1d07b0e9708451fd657e7812327ec6501	USDC	1,231,545.17	<unknown ERC-4626>	<unknown>	<unknown>	1200726.03948	2024-12-12 13:30:29	ERC4262VaultDetection(chain=42161, address='0x...	{'name': 'USD Coin', 'symbol': 'USDC', 'total_...	{'name': 'fija Strategy USDC GMXv2', 'symbol':...	Arbitrum	False	True	False	False	False	1296	270	1566	224 days 23:00:57.677211
fija Strategy crvUSD CurveConvex	scrvUSD_CC	0x6155c331aa73a9872b22871eb6d7516c6fb52a69	crvUSD	1,073,580.29	<unknown ERC-4626>	<unknown>	<unknown>	939563.649206483304142095	2023-10-19 13:01:47	ERC4262VaultDetection(chain=1, address='0x6155...	{'name': 'Curve.Fi USD Stablecoin', 'symbol': ...	{'name': 'fija Strategy crvUSD CurveConvex', '...	Ethereum	False	True	False	False	False	41	16	57	644 days 23:29:39.677211
fija USDC GMXv2 RY	fUSDC_GMXv2	0xf5b7ac0afbe08ffa7b4bd9bd4e6d678201356658	USDC	223,035.20	<unknown ERC-4626>	<unknown>	<unknown>	225232.348031	2024-10-30 09:21:01	ERC4262VaultDetection(chain=42161, address='0x...	{'name': 'USD Coin', 'symbol': 'USDC', 'total_...	{'name': 'fija USDC GMXv2 RY', 'symbol': 'fUSD...	Arbitrum	False	True	False	False	False	755	177	932	268 days 03:10:25.677211
fija USDC GMXv2 FIAX	fUSDC	0x4f7f56c6833b7ae211f8cd2563d0728252b2b088	USDC	18,580.67	<unknown ERC-4626>	<unknown>	<unknown>	18899.453822	2025-05-02 10:20:31	ERC4262VaultDetection(chain=42161, address='0x...	{'name': 'USD Coin', 'symbol': 'USDC', 'total_...	{'name': 'fija USDC GMXv2 FIAX', 'symbol': 'fU...	Arbitrum	False	True	False	False	False	124	112	236	84 days 02:10:55.677211

Grab the filtered in vault ids for the rest of the notebook.

VAULTS = [spec for spec, row in leads.items() if spec.vault_address in vault_df["Address"].values]

print(f"Examining {len(VAULTS):,} vaults.")

Examining 4 vaults.

Calculate reports

• Calculate vault reports for all vaults

from eth_defi.research.vault_metrics import analyse_vault

reports = {}
for vault_spec in VAULTS:

    vault_report = analyse_vault(
        vault_db=vault_db,
        prices_df=prices_df,
        spec=vault_spec,
        chart_frequency="daily",
    )

    if vault_report is not None:
        reports[vault_spec] = vault_report

Examining vault fija USDC GMXv2 RY: 42161-0xf5b7ac0afbe08ffa7b4bd9bd4e6d678201356658, having 5,415 raw returns, 5,414 hourly and 235 daily returns
Share price movement: 1.0090 2024-10-30 09:34:08 -> 0.9901 2025-07-19 14:36:29
Examining vault fija Strategy USDC GMXv2: 42161-0xadbb6ec1d07b0e9708451fd657e7812327ec6501, having 4,967 raw returns, 4,966 hourly and 219 daily returns
Share price movement: 0.9988 2024-12-12 13:32:24 -> 1.0256 2025-07-19 14:36:29
Examining vault fija USDC GMXv2 FIAX: 42161-0x4f7f56c6833b7ae211f8cd2563d0728252b2b088, having 1,172 raw returns, 1,171 hourly and 74 daily returns
Share price movement: 0.9998 2025-05-02 11:19:13 -> 0.9830 2025-07-19 14:36:29
Examining vault fija Strategy crvUSD CurveConvex: 1-0x6155c331aa73a9872b22871eb6d7516c6fb52a69, having 13,110 raw returns, 13,109 hourly and 551 daily returns
Share price movement: 0.9999 2023-10-19 13:54:11 -> 1.1274 2025-05-16 08:37:59

Comparison table for portfolio key metrics

• Create a comparison matrix of key metrics, each vault being one column

from eth_defi.research.vault_metrics import format_vault_header
from eth_defi.research.vault_metrics import format_ffn_performance_stats

columns = []

for spec, report in reports.items():
    vault_row = vault_df[vault_df["Address"] == spec.vault_address].iloc[0]
    vault_row["Name"] = vault_row.name  # Index remaap
    header = format_vault_header(vault_row)
    column_series = format_ffn_performance_stats(report.performance_stats, prefix_series=header)
    columns.append(column_series)

# print(columns)

df = pd.DataFrame(columns).set_index("Name").T
display(df)

Name	fija USDC GMXv2 RY	fija Strategy USDC GMXv2	fija USDC GMXv2 FIAX	fija Strategy crvUSD CurveConvex
Chain	Arbitrum	Arbitrum	Arbitrum	Ethereum
Address	0xf5b7ac0afbe08ffa7b4bd9bd4e6d678201356658	0xadbb6ec1d07b0e9708451fd657e7812327ec6501	0x4f7f56c6833b7ae211f8cd2563d0728252b2b088	0x6155c331aa73a9872b22871eb6d7516c6fb52a69
Denomination	USDC	USDC	USDC	crvUSD
NAV	223,035.20	1,231,545.17	18,580.67	1,073,580.29
First seen	2024-10-30 09:21:01	2024-12-12 13:30:29	2025-05-02 10:20:31	2023-10-19 13:01:47
Total events	932	1566	236	57
Age	268 days 03:10:25.677211	224 days 23:00:57.677211	84 days 02:10:55.677211	644 days 23:29:39.677211
Start	2024-10-30	2024-12-12	2025-05-02	2023-10-19
End	2025-07-19	2025-07-19	2025-07-19	2025-05-16
Risk-free rate	0.00%	0.00%	0.00%	0.00%
Total Return	-1.86%	2.74%	-1.68%	12.75%
CAGR	-2.59%	4.61%	-7.62%	7.92%
Max Drawdown	-8.85%	-3.16%	-1.99%	-0.20%
Calmar Ratio	-0.29	1.46	-3.84	40.43
MTD	-1.35%	-1.35%	-1.35%	0.41%
3m	-1.90%	-1.90%	-	2.21%
6m	-2.44%	-0.77%	-	4.99%
YTD	-1.94%	-0.04%	-1.68%	3.02%
1Y	-	-	-	8.64%
3Y (ann.)	-	-	-	7.92%
5Y (ann.)	-	-	-	-
10Y (ann.)	-	-	-	-
Since Incep. (ann.)	-2.59%	4.61%	-7.62%	7.92%
Daily Sharpe	-0.13	0.87	-1.55	2.93
Daily Sortino	-0.18	1.47	-2.01	23.05
Daily Mean (ann.)	-1.43%	3.17%	-5.69%	5.51%
Daily Vol (ann.)	10.88%	3.63%	3.67%	1.88%
Daily Skew	-0.79	0.92	-1.30	6.23
Daily Kurt	95.07	10.60	2.78	45.47
Best Day	6.84%	1.49%	0.46%	1.29%
Worst Day	-7.24%	-0.86%	-0.77%	-0.10%
Monthly Sharpe	-0.82	0.00	-1.74	7.42
Monthly Sortino	-1.36	0.00	-2.04	inf
Monthly Mean (ann.)	-2.47%	0.00%	-6.76%	7.61%
Monthly Vol (ann.)	3.02%	4.17%	3.87%	1.03%
Monthly Skew	0.48	0.47	-	0.34
Monthly Kurt	-0.20	-1.25	-	1.13
Best Month	1.36%	1.76%	0.23%	1.32%
Worst Month	-1.35%	-1.35%	-1.35%	0.04%
Yearly Sharpe	-	-	-	1.43
Yearly Sortino	-	-	-	inf
Yearly Mean	-1.94%	-0.04%	-	5.96%
Yearly Vol	-	-	-	4.16%
Yearly Skew	-	-	-	-
Yearly Kurt	-	-	-	-
Best Year	-1.94%	-0.04%	-	8.90%
Worst Year	-1.94%	-0.04%	-	3.02%
Avg. Drawdown	-1.96%	-0.34%	-1.83%	-0.04%
Avg. Drawdown Days	37.67	13.07	35.00	5.34
Avg. Up Month	0.76%	1.12%	0.23%	0.63%
Avg. Down Month	-0.69%	-0.84%	-1.35%	-
Win Year %	0.00%	0.00%	-	100.00%
Win 12m %	-	-	-	100.00%

Vault charts

• Examine vault metrics and charts

from eth_defi.research.vault_metrics import analyse_vault

for spec, vault_report in reports.items():

    # Display returns figur
    returns_chart_fig = vault_report.rolling_returns_chart
    returns_chart_fig.show()

Returns comparison chart

• Show rolling returns of all picked vaults

from eth_defi.research.rolling_returns import calculate_rolling_returns, visualise_rolling_returns

interesting_vaults = [spec.as_string_id() for spec in VAULTS]

rolling_returns_df = calculate_rolling_returns(
    prices_df,
    interesting_vaults=interesting_vaults,
    clip_up=50,
)

# display(rolling_returns_df.head(3))

fig = visualise_rolling_returns(rolling_returns_df)

fig.show()