### Install tefas-crawler

Source: https://github.com/burakyilmaz321/tefas-crawler/blob/main/README.md

Install the tefas-crawler library using pip.

```bash
pip install tefas-crawler
```

--------------------------------

### Fetch All Funds for a Day

Source: https://github.com/burakyilmaz321/tefas-crawler/blob/main/README.md

Retrieve all fund information for a specific date. The `start` parameter is required.

```python
data = tefas.fetch(start="2020-11-20")
```

--------------------------------

### Snap to Valid API Periods with _months_back

Source: https://context7.com/burakyilmaz321/tefas-crawler/llms.txt

Internal helper that converts a start date to the nearest valid API `periyod` value from the set {1, 3, 6, 12, 36, 60}. Dates older than 5 years are clamped to 60 months.

```python
from datetime import date
from dateutil.relativedelta import relativedelta
from tefas.crawler import _months_back

today = date.today()

# 2 weeks ago → snaps up to 1 month
print(_months_back(today - relativedelta(weeks=2)))   # 1

# 2 months ago → snaps up to 3 months
print(_months_back(today - relativedelta(months=2)))  # 3

# 7 months ago → snaps up to 12 months
print(_months_back(today - relativedelta(months=7)))  # 12

# 4 years ago → snaps up to 60 months (max)
print(_months_back(today - relativedelta(years=4)))   # 60
```

--------------------------------

### Fetch Single Fund Data Using Datetime Objects

Source: https://context7.com/burakyilmaz321/tefas-crawler/llms.txt

Retrieve data for a single fund ('ATA') within a date range, using Python datetime objects for the start and end dates.

```python
# --- 4. datetime objects as input ---
df_dt = tefas.fetch(
    start=datetime(2025, 2, 1),
    end=datetime(2025, 2, 28),
    name="ATA",
)
```

--------------------------------

### Fetch Specific Fund Data with Columns

Source: https://github.com/burakyilmaz321/tefas-crawler/blob/main/README.md

Retrieve data for a specific fund within a date range, selecting only the desired columns. The `start`, `end`, and `name` parameters are used to filter the data, and `columns` specifies which fields to return.

```python
data = tefas.fetch(start="2020-11-15", end="2020-11-20", name="YAC", columns=["code", "date", "price"])
```

--------------------------------

### Fetch Single Fund Data by Date Range (String Dates)

Source: https://context7.com/burakyilmaz321/tefas-crawler/llms.txt

Retrieve daily price data for a single fund ('YAC') within a specified date range using string inputs for start and end dates. The output includes columns like 'date', 'code', 'title', 'price', 'category_rank', and 'category_total'.

```python
import warnings
from datetime import datetime
from tefas import Crawler

tefas = Crawler()

# --- 1. Single fund, date range, string dates ---
df = tefas.fetch(start="2025-01-01", end="2025-01-31", name="YAC")
print(df.columns.tolist())
# ['date', 'code', 'title', 'price', 'category_rank', 'category_total']
print(df.head(3))
#         date code                         title   price  category_rank  category_total
# 0 2025-01-02  YAC  Yapı Kredi Asset Mgmt Fund   1.2345              5              80
# 1 2025-01-03  YAC  Yapı Kredi Asset Mgmt Fund   1.2401              5              80
# 2 2025-01-06  YAC  Yapı Kredi Asset Mgmt Fund   1.2389              4              80
```

--------------------------------

### Initialize Crawler

Source: https://github.com/burakyilmaz321/tefas-crawler/blob/main/README.md

Import the Crawler object and create an instance to begin crawling.

```python
from tefas import Crawler

tefas = Crawler()
```

--------------------------------

### Initialize Crawler with Default and Custom Fund Limit

Source: https://context7.com/burakyilmaz321/tefas-crawler/llms.txt

Instantiate the Crawler class. The default fund limit is 50. You can increase this limit to fetch more funds in bulk.

```python
from tefas import Crawler

# Default: fan-out capped at 50 funds
tefas = Crawler()

# Raise the cap to fetch up to 200 funds in bulk
tefas_large = Crawler(fund_limit=200)

print(tefas.fund_limit)   # 50
print(tefas_large.fund_limit)  # 200
```

--------------------------------

### Crawler Constructor

Source: https://context7.com/burakyilmaz321/tefas-crawler/llms.txt

Initializes the Crawler class, establishing an HTTP session and setting an optional fund limit for bulk fetches.

```APIDOC
## Crawler(fund_limit=50) — Constructor

Creates a persistent HTTP session targeting `https://www.tefas.gov.tr`. The optional `fund_limit` parameter controls how many funds are fetched when `fetch()` is called without a `name` argument. Increase it to retrieve more funds in bulk, keeping in mind that each fund triggers a separate HTTP request.

```python
from tefas import Crawler

# Default: fan-out capped at 50 funds
tefas = Crawler()

# Raise the cap to fetch up to 200 funds in bulk
tefas_large = Crawler(fund_limit=200)

print(tefas.fund_limit)   # 50
print(tefas_large.fund_limit)  # 200
```
```

--------------------------------

### Bulk Fetch Funds and Handle Warnings

Source: https://context7.com/burakyilmaz321/tefas-crawler/llms.txt

Perform a bulk fetch of funds without specifying a name. This action emits a UserWarning and is capped by `fund_limit` (default 50). The output confirms the number of unique fund codes fetched.

```python
# --- 7. Bulk fetch (no name) — emits UserWarning, capped at fund_limit ---
with warnings.catch_warnings(record=True) as caught:
    warnings.simplefilter("always")
    df_bulk = tefas.fetch(start="2025-04-01", kind="YAT")
    print(caught[0].category.__name__)  # UserWarning
print(len(df_bulk["code"].unique()))    # up to 50 unique fund codes
```

--------------------------------

### Crawler.fetch Method

Source: https://context7.com/burakyilmaz321/tefas-crawler/llms.txt

Fetches daily price data for specified funds and date ranges. Supports string or datetime objects for dates, and allows filtering by fund type and selecting specific columns.

```APIDOC
## Crawler.fetch(start, end, name, columns, kind) — Fetch Fund Data

The primary API. Returns a `pandas.DataFrame` containing daily price rows for the requested fund(s) and date range. `start` and `end` accept either ISO date strings (`"YYYY-MM-DD"`) or `datetime.datetime` / `datetime.date` objects. The `kind` parameter filters by fund type: `"YAT"` (investment funds, default), `"EMK"` (commodity funds), or `"BYF"` (exchange-traded funds). Columns no longer published by the new API (e.g. `market_cap`, `number_of_shares`, asset-allocation breakdowns) are silently dropped with a `UserWarning` rather than raising an error.

```python
import warnings
from datetime import datetime
from tefas import Crawler

tefas = Crawler()

# --- 1. Single fund, date range, string dates ---
df = tefas.fetch(start="2025-01-01", end="2025-01-31", name="YAC")
print(df.columns.tolist())
# ['date', 'code', 'title', 'price', 'category_rank', 'category_total']
print(df.head(3))
#         date code                         title   price  category_rank  category_total
# 0 2025-01-02  YAC  Yapı Kredi Asset Mgmt Fund   1.2345              5              80
# 1 2025-01-03  YAC  Yapı Kredi Asset Mgmt Fund   1.2401              5              80
# 2 2025-01-06  YAC  Yapı Kredi Asset Mgmt Fund   1.2389              4              80

# --- 2. Single fund, single day ---
df_day = tefas.fetch(start="2025-04-01", name="YAC")
print(df_day[["date", "price"]])
#         date   price
# 0 2025-04-01  1.3112

# --- 3. Select specific columns ---
df_slim = tefas.fetch(
    start="2025-01-01",
    end="2025-03-31",
    name="TI2",
    columns=["code", "date", "price"],
)
print(df_slim.dtypes)
# code     object
# date     object
# price    float64

# --- 4. datetime objects as input ---
df_dt = tefas.fetch(
    start=datetime(2025, 2, 1),
    end=datetime(2025, 2, 28),
    name="ATA",
)

# --- 5. Exchange-traded fund type (BYF) ---
df_byf = tefas.fetch(start="2025-01-15", end="2025-01-31", name="GLDTR", kind="BYF")

# --- 6. Requesting a deprecated column triggers a warning, not an error ---
with warnings.catch_warnings(record=True) as caught:
    warnings.simplefilter("always")
    df_warn = tefas.fetch(
        start="2025-04-01",
        name="YAC",
        columns=["code", "date", "price", "market_cap"],  # market_cap is gone
    )
    if caught:
        print(caught[0].message)
        # Columns no longer exposed by the tefas.gov.tr API and omitted: ['market_cap']
print(df_warn.columns.tolist())
# ['code', 'date', 'price']

# --- 7. Bulk fetch (no name) — emits UserWarning, capped at fund_limit ---
with warnings.catch_warnings(record=True) as caught:
    warnings.simplefilter("always")
    df_bulk = tefas.fetch(start="2025-04-01", kind="YAT")
    print(caught[0].category.__name__)  # UserWarning
print(len(df_bulk["code"].unique()))    # up to 50 unique fund codes

# --- 8. Empty result when API returns no data ---
df_empty = tefas.fetch(start="2015-01-01", name="NONEXISTENT")
print(df_empty.empty)  # True
print(df_empty.columns.tolist())
# ['date', 'code', 'title', 'price', 'category_rank', 'category_total']
```
```

--------------------------------

### Fetch Single Fund Data for a Single Day

Source: https://context7.com/burakyilmaz321/tefas-crawler/llms.txt

Retrieve price data for a single fund ('YAC') for a specific day. Only the 'date' and 'price' columns are printed.

```python
# --- 2. Single fund, single day ---
df_day = tefas.fetch(start="2025-04-01", name="YAC")
print(df_day[["date", "price"]])
#         date   price
# 0 2025-04-01  1.3112
```

--------------------------------

### Handle Empty Results

Source: https://context7.com/burakyilmaz321/tefas-crawler/llms.txt

Fetch data for a non-existent fund ('NONEXISTENT'). The result is an empty DataFrame, indicated by `df_empty.empty` being True. The columns of the empty DataFrame are still listed.

```python
# --- 8. Empty result when API returns no data ---
df_empty = tefas.fetch(start="2015-01-01", name="NONEXISTENT")
print(df_empty.empty)  # True
print(df_empty.columns.tolist())
# ['date', 'code', 'title', 'price', 'category_rank', 'category_total']
```

--------------------------------

### Fetch Single Fund Data with Specific Columns

Source: https://context7.com/burakyilmaz321/tefas-crawler/llms.txt

Retrieve data for a single fund ('TI2') within a date range, selecting only 'code', 'date', and 'price' columns. The output shows the data types of the selected columns.

```python
# --- 3. Select specific columns ---
df_slim = tefas.fetch(
    start="2025-01-01",
    end="2025-03-31",
    name="TI2",
    columns=["code", "date", "price"],
)
print(df_slim.dtypes)
# code     object
# date     object
# price    float64
```

--------------------------------

### Handle Deprecated Columns with Warnings

Source: https://context7.com/burakyilmaz321/tefas-crawler/llms.txt

Attempt to fetch data including a deprecated column ('market_cap'). The library catches this and issues a UserWarning, omitting the column rather than raising an error. The output shows the available columns after omitting the deprecated one.

```python
# --- 6. Requesting a deprecated column triggers a warning, not an error ---
with warnings.catch_warnings(record=True) as caught:
    warnings.simplefilter("always")
    df_warn = tefas.fetch(
        start="2025-04-01",
        name="YAC",
        columns=["code", "date", "price", "market_cap"],  # market_cap is gone
    )
    if caught:
        print(caught[0].message)
        # Columns no longer exposed by the tefas.gov.tr API and omitted: ['market_cap']
print(df_warn.columns.tolist())
# ['code', 'date', 'price']
```

--------------------------------

### Fetch Exchange-Traded Fund (BYF) Data

Source: https://context7.com/burakyilmaz321/tefas-crawler/llms.txt

Retrieve data for an exchange-traded fund ('GLDTR') within a specified date range by setting the 'kind' parameter to 'BYF'.

```python
# --- 5. Exchange-traded fund type (BYF) ---
df_byf = tefas.fetch(start="2025-01-15", end="2025-01-31", name="GLDTR", kind="BYF")
```

--------------------------------

### fetch

Source: https://github.com/burakyilmaz321/tefas-crawler/blob/main/README.md

Fetches investment fund information from TEFAS. It can retrieve data for all funds or a specific fund within a given date range and allows for column selection and fund type filtering.

```APIDOC
## fetch(start, end, name, columns, kind)

### Description
Fetches investment fund information from TEFAS. It can retrieve data for all funds or a specific fund within a given date range and allows for column selection and fund type filtering.

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
- **start** (string or datetime.datetime) - Required - The date that fund information is crawled for.
- **end** (string or datetime.datetime) - Optional - End of the period that fund information is crawled for.
- **name** (string) - Optional - Name of the fund. If not given, all funds will be returned.
- **columns[]** (list of string) - Optional - List of columns to be returned.
- **kind** (string) - Optional - Type of the fund. One of `YAT`, `EMK`, or `BYF`. Defaults to `YAT`.

### Request Example
```python
data = tefas.fetch(start="2020-11-20")
```

### Request Example 2
```python
data = tefas.fetch(start="2020-11-15", end="2020-11-20", name="YAC", columns=["code", "date", "price"])
```

### Response
#### Success Response (200)
- **data** (object) - Fund information data.

#### Response Example
```json
{
  "example": "[Fund data]"
}
```
```

--------------------------------

### Parse Dates with _parse_date Utility

Source: https://context7.com/burakyilmaz321/tefas-crawler/llms.txt

Internal helper to normalize string, datetime.datetime, or datetime.date inputs to a datetime.date object. Raises ValueError for unsupported formats.

```python
from datetime import date, datetime
from tefas.crawler import _parse_date

# String input
print(_parse_date("2025-04-15"))        # datetime.date(2025, 4, 15)

# datetime input (time component is stripped)
print(_parse_date(datetime(2025, 4, 15, 12, 30)))  # datetime.date(2025, 4, 15)

# date input (pass-through)
print(_parse_date(date(2025, 4, 15)))   # datetime.date(2025, 4, 15)

# Bad format raises ValueError
try:
    _parse_date("15-04-2025")
except ValueError as e:
    print(e)  # Date string format is incorrect. It should be `YYYY-MM-DD`
```

--------------------------------

### Deserialize TEFAS API Data with InfoSchema

Source: https://context7.com/burakyilmaz321/tefas-crawler/llms.txt

Use InfoSchema to map raw JSON keys from the TEFAS API to clean Python field names. Unknown keys are excluded by default. Supports bulk deserialization with `many=True`.

```python
from tefas.schema import InfoSchema, BreakdownSchema

schema = InfoSchema()

# Inspect available fields
print(list(schema.fields.keys()))
# ['date', 'code', 'title', 'price', 'category_rank', 'category_total']

# Deserialize a raw API response row manually
raw_row = {
    "tarih": "2025-04-01",
    "fonKodu": "YAC",
    "fonUnvan": "Yapı Kredi Varlık Yönetimi Fonu",
    "fiyat": 1.3112,
    "kategoriDerece": 5,
    "kategoriFonSay": 80,
}
result = schema.load(raw_row)
print(result)
# {'date': datetime.date(2025, 4, 1), 'code': 'YAC',
#  'title': 'Yapı Kredi Varlık Yönetimi Fonu', 'price': 1.3112,
#  'category_rank': 5, 'category_total': 80}

# Unknown keys are silently excluded (Meta: unknown = EXCLUDE)
raw_with_extras = {**raw_row, "bilinmeyen": "ignored"}
clean = schema.load(raw_with_extras)
print("bilinmeyen" in clean)  # False

# Bulk deserialization (many=True)
bulk_schema = InfoSchema(many=True)
rows = bulk_schema.load([raw_row, raw_row])
print(len(rows))  # 2

# BreakdownSchema is a no-op stub — loads nothing, errors on nothing
breakdown = BreakdownSchema()
print(breakdown.load({"anything": 123}))  # {}
```

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