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Support for Hedge Fund Monitor from the Office of Financial Research
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Timothy Kassis
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scientific-skills/hedgefundmonitor/references/examples.md
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scientific-skills/hedgefundmonitor/references/examples.md
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# Code Examples
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## Installation
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```bash
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uv add requests pandas matplotlib
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```
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## 1. Discover Available Data
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```python
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import requests
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import pandas as pd
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BASE = "https://data.financialresearch.gov/hf/v1"
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# List all datasets
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resp = requests.get(f"{BASE}/series/dataset")
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for key, info in resp.json().items():
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print(f"{key}: {info['long_name']}")
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# List all mnemonics for FPF with names
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resp = requests.get(f"{BASE}/metadata/mnemonics", params={"dataset": "fpf"})
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mnemonics = pd.DataFrame(resp.json())
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print(mnemonics.head(20))
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# Search for leverage-related series
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resp = requests.get(f"{BASE}/metadata/search", params={"query": "*leverage*"})
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results = pd.DataFrame(resp.json())
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# Deduplicate to get unique mnemonics
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leverage_series = results[results["mnemonic"] != "none"]["mnemonic"].unique()
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print(leverage_series)
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```
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## 2. Fetch and Plot Hedge Fund Leverage Over Time
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```python
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import requests
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import pandas as pd
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import matplotlib.pyplot as plt
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BASE = "https://data.financialresearch.gov/hf/v1"
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# Fetch overall leverage ratio
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resp = requests.get(f"{BASE}/series/timeseries", params={
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"mnemonic": "FPF-ALLQHF_LEVERAGERATIO_GAVWMEAN",
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"remove_nulls": "true"
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})
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df = pd.DataFrame(resp.json(), columns=["date", "leverage"])
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df["date"] = pd.to_datetime(df["date"])
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# Get metadata
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meta_resp = requests.get(f"{BASE}/metadata/query", params={
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"mnemonic": "FPF-ALLQHF_LEVERAGERATIO_GAVWMEAN",
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"fields": "description/name,schedule/observation_frequency"
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})
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meta = meta_resp.json()
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title = meta["description"]["name"]
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plt.figure(figsize=(12, 5))
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plt.plot(df["date"], df["leverage"], linewidth=2)
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plt.title(title)
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plt.ylabel("Leverage Ratio")
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plt.grid(True, alpha=0.3)
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plt.tight_layout()
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plt.savefig("hedge_fund_leverage.png", dpi=150)
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```
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## 3. Compare Strategy-Level Leverage
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```python
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import requests
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import pandas as pd
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import matplotlib.pyplot as plt
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BASE = "https://data.financialresearch.gov/hf/v1"
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strategies = {
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"All Funds": "FPF-ALLQHF_LEVERAGERATIO_GAVWMEAN",
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"Equity": "FPF-STRATEGY_EQUITY_LEVERAGERATIO_GAVWMEAN",
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"Credit": "FPF-STRATEGY_CREDIT_LEVERAGERATIO_GAVWMEAN",
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"Macro": "FPF-STRATEGY_MACRO_LEVERAGERATIO_GAVWMEAN",
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}
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resp = requests.get(f"{BASE}/series/multifull", params={
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"mnemonics": ",".join(strategies.values()),
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"remove_nulls": "true"
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})
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results = resp.json()
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fig, ax = plt.subplots(figsize=(14, 6))
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for label, mne in strategies.items():
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ts = results[mne]["timeseries"]["aggregation"]
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df = pd.DataFrame(ts, columns=["date", "value"])
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df["date"] = pd.to_datetime(df["date"])
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ax.plot(df["date"], df["value"], label=label, linewidth=2)
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ax.set_title("Hedge Fund Leverage by Strategy (GAV-Weighted)")
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ax.set_ylabel("Leverage Ratio")
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ax.legend()
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ax.grid(True, alpha=0.3)
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plt.tight_layout()
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plt.savefig("leverage_by_strategy.png", dpi=150)
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```
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## 4. Download Full FPF Dataset into a Wide DataFrame
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```python
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import requests
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import pandas as pd
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BASE = "https://data.financialresearch.gov/hf/v1"
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# Download entire FPF dataset, recent data only
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resp = requests.get(f"{BASE}/series/dataset", params={
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"dataset": "fpf",
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"start_date": "2015-01-01",
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"remove_nulls": "false"
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})
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data = resp.json()
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# Build a wide DataFrame with one column per series
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frames = {}
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for mne, series_data in data["timeseries"].items():
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ts = series_data["timeseries"]["aggregation"]
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if ts:
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s = pd.Series(
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{row[0]: row[1] for row in ts},
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name=mne
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)
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frames[mne] = s
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df = pd.DataFrame(frames)
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df.index = pd.to_datetime(df.index)
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df = df.sort_index()
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print(f"Shape: {df.shape}") # (dates, series)
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print(df.tail())
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```
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## 5. Stress Test Analysis
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```python
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import requests
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import pandas as pd
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BASE = "https://data.financialresearch.gov/hf/v1"
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# CDS stress test scenarios (P5 = 5th percentile fund, P50 = median fund)
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stress_mnemonics = [
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"FPF-ALLQHF_CDSDOWN250BPS_P5",
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"FPF-ALLQHF_CDSDOWN250BPS_P50",
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"FPF-ALLQHF_CDSUP250BPS_P5",
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"FPF-ALLQHF_CDSUP250BPS_P50",
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]
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resp = requests.get(f"{BASE}/series/multifull", params={
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"mnemonics": ",".join(stress_mnemonics),
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"remove_nulls": "true"
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})
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results = resp.json()
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frames = []
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for mne in stress_mnemonics:
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ts = results[mne]["timeseries"]["aggregation"]
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name = results[mne]["metadata"]["description"]["name"]
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df = pd.DataFrame(ts, columns=["date", mne])
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df["date"] = pd.to_datetime(df["date"])
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df = df.set_index("date")
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frames.append(df)
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stress_df = pd.concat(frames, axis=1)
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stress_df.columns = [r["metadata"]["description"]["name"]
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for r in [results[m] for m in stress_mnemonics]]
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print(stress_df.tail(8).to_string())
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```
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## 6. FICC Sponsored Repo Volume Trend
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```python
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import requests
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import pandas as pd
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import matplotlib.pyplot as plt
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BASE = "https://data.financialresearch.gov/hf/v1"
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resp = requests.get(f"{BASE}/series/multifull", params={
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"mnemonics": "FICC-SPONSORED_REPO_VOL,FICC-SPONSORED_REVREPO_VOL",
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"remove_nulls": "true"
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})
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results = resp.json()
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fig, ax = plt.subplots(figsize=(12, 5))
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for mne, label in [
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("FICC-SPONSORED_REPO_VOL", "Repo Volume"),
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("FICC-SPONSORED_REVREPO_VOL", "Reverse Repo Volume"),
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]:
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ts = results[mne]["timeseries"]["aggregation"]
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df = pd.DataFrame(ts, columns=["date", "value"])
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df["date"] = pd.to_datetime(df["date"])
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# Convert to trillions
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df["value"] = df["value"] / 1e12
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ax.plot(df["date"], df["value"], label=label, linewidth=2)
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ax.set_title("FICC Sponsored Repo Service Volumes")
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ax.set_ylabel("Trillions USD")
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ax.legend()
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ax.grid(True, alpha=0.3)
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plt.tight_layout()
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plt.savefig("ficc_repo_volumes.png", dpi=150)
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```
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## 7. Download Category CSV
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```python
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import requests
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import io
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import pandas as pd
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BASE = "https://data.financialresearch.gov/hf/v1"
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# Download the leverage category as a DataFrame
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resp = requests.get(f"{BASE}/categories", params={"category": "leverage"})
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df = pd.read_csv(io.StringIO(resp.text))
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print(df.head())
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# All categories: complexity, counterparties, leverage, liquidity, risk_management, size
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```
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## 8. Counterparty Concentration Analysis
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```python
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import requests
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import pandas as pd
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BASE = "https://data.financialresearch.gov/hf/v1"
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# Top 8 counterparties lending to all qualifying hedge funds
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party_mnemonics = [f"FPF-ALLQHF_PARTY{i}_SUM" for i in range(1, 9)]
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resp = requests.get(f"{BASE}/series/multifull", params={
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"mnemonics": ",".join(party_mnemonics),
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"remove_nulls": "false"
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})
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results = resp.json()
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# Get the most recent quarter's values
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frames = []
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for mne in party_mnemonics:
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ts = results[mne]["timeseries"]["aggregation"]
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df = pd.DataFrame(ts, columns=["date", "value"])
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df["date"] = pd.to_datetime(df["date"])
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df["mnemonic"] = mne
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frames.append(df)
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all_data = pd.concat(frames).pivot(index="date", columns="mnemonic", values="value")
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print("Most recent quarter counterparty exposure (USD billions):")
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print((all_data.iloc[-1] / 1e9).sort_values(ascending=False).to_string())
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```
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## 9. Periodic Refresh Pattern
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```python
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import requests
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import pandas as pd
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from datetime import datetime, timedelta
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BASE = "https://data.financialresearch.gov/hf/v1"
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def get_recent_fpf(days_back: int = 180) -> pd.DataFrame:
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"""Fetch only the most recent FPF observations (for periodic refreshes)."""
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start = (datetime.today() - timedelta(days=days_back)).strftime("%Y-%m-%d")
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resp = requests.get(f"{BASE}/series/dataset", params={
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"dataset": "fpf",
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"start_date": start,
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"remove_nulls": "true"
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})
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data = resp.json()
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frames = {}
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for mne, series_data in data["timeseries"].items():
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ts = series_data["timeseries"]["aggregation"]
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if ts:
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frames[mne] = pd.Series({row[0]: row[1] for row in ts}, name=mne)
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return pd.DataFrame(frames)
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recent = get_recent_fpf(days_back=365)
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print(recent.shape)
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```
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