diff --git a/AssetAnalysis/LICENSE b/AssetAnalysis/LICENSE
new file mode 100644
index 0000000..6088fce
--- /dev/null
+++ b/AssetAnalysis/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Vahid Naderi
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/AssetAnalysis/analysis.ipynb b/AssetAnalysis/analysis.ipynb
new file mode 100644
index 0000000..a56a01c
--- /dev/null
+++ b/AssetAnalysis/analysis.ipynb
@@ -0,0 +1,1008 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "972102d4",
+ "metadata": {},
+ "source": [
+ "import and read Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "d142afdd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd \n",
+ "import numpy as np \n",
+ "from common.utils import daily_to_monthly , calculate_change\n",
+ "from automotive.automotive import generate_car_index\n",
+ "from real_estate.real_estates import generate_housing_index\n",
+ "from functools import reduce\n",
+ "import seaborn as sns\n",
+ "import matplotlib.pyplot as plt\n",
+ "from gold.gold import generate_missing_gold\n",
+ "from stock.stocks import generate_missing_stock\n",
+ "from coin.gold_coin import generate_missing_imami\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "a33312d1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " Date \n",
+ " Price \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 1360/07 \n",
+ " 270.0 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 1360/08 \n",
+ " 270.0 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 1360/09 \n",
+ " 270.0 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 1360/10 \n",
+ " 270.0 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 1360/11 \n",
+ " 270.0 \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 505 \n",
+ " 1402/08 \n",
+ " 511658.0 \n",
+ " \n",
+ " \n",
+ " 506 \n",
+ " 1402/09 \n",
+ " 504426.0 \n",
+ " \n",
+ " \n",
+ " 507 \n",
+ " 1402/10 \n",
+ " 514272.0 \n",
+ " \n",
+ " \n",
+ " 508 \n",
+ " 1402/11 \n",
+ " 559332.0 \n",
+ " \n",
+ " \n",
+ " 509 \n",
+ " 1402/12 \n",
+ " 591460.0 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
510 rows × 2 columns
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "dfs = [\n",
+ " df_usd[[\"Date\", \"Price\"]].rename(columns={\"Price\": \"usd\"}),\n",
+ " df_gold[[\"Date\", \"Price\"]].rename(columns={\"Price\": \"gold\"}),\n",
+ " df_imami[[\"Date\", \"Price\"]].rename(columns={\"Price\": \"imami\"}),\n",
+ " df_stock[[\"Date\", \"Price\"]].rename(columns={\"Price\": \"stock\"}),\n",
+ "]\n",
+ "\n",
+ "\n",
+ "df_merged = reduce(\n",
+ " lambda left, right: pd.merge(left, right, on=\"Date\", how=\"inner\"),\n",
+ " dfs\n",
+ ")\n",
+ "\n",
+ "corr_matrix = df_merged.drop(columns=\"Date\").corr()\n",
+ "print(corr_matrix.round(3))\n",
+ "\n",
+ "plt.figure(figsize=(8, 6))\n",
+ "sns.heatmap(\n",
+ " corr_matrix,\n",
+ " annot=True,\n",
+ " cmap=\"coolwarm\",\n",
+ " center=0,\n",
+ " square=True,\n",
+ " fmt=\".3f\"\n",
+ ")\n",
+ "plt.title(\"Correlation Matrix (1393–1402)\", fontsize=14)\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "4b65e398",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "iamami / usd = 383.4\n",
+ "gold / usd = 48.5\n",
+ "stock / usd = 3.1\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "merged = df_usd[[\"Date\", \"Price\"]].rename(columns={\"Price\": \"usd\"})\n",
+ "merged = merged.merge(df_imami[[\"Date\", \"Price\"]].rename(columns={\"Price\": \"imami\"}), on=\"Date\")\n",
+ "merged = merged.merge(df_gold[[\"Date\", \"Price\"]].rename(columns={\"Price\": \"gold\"}), on=\"Date\")\n",
+ "merged = merged.merge(df_stock[[\"Date\", \"Price\"]].rename(columns={\"Price\": \"stock\"}), on=\"Date\")\n",
+ "\n",
+ "\n",
+ "imami_usd = (merged[\"imami\"] / merged[\"usd\"]).mean()\n",
+ "gold_usd = (merged[\"gold\"] / merged[\"usd\"]).mean()\n",
+ "stock_usd = (merged[\"stock\"] / merged[\"usd\"]).mean()\n",
+ "\n",
+ "print(f\"iamami / usd = {imami_usd:.1f}\")\n",
+ "print(f\"gold / usd = {gold_usd:.1f}\")\n",
+ "print(f\"stock / usd = {stock_usd:.1f}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "07a29157",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "month len 513\n",
+ "first m 1360/07\n",
+ "last m 1403/03\n"
+ ]
+ }
+ ],
+ "source": [
+ "missing_gold = generate_missing_gold(\n",
+ " usd_df=df_usd, \n",
+ " start_date=\"1393/02\", \n",
+ " ratio=48.5\n",
+ ")\n",
+ "\n",
+ "\n",
+ "df_gold_full = pd.concat(\n",
+ " [missing_gold, df_gold],\n",
+ " ignore_index=True\n",
+ ")\n",
+ "\n",
+ "\n",
+ "df_gold_full = df_gold_full.sort_values(\"Date\").reset_index(drop=True)\n",
+ "\n",
+ "\n",
+ "print(\"month len \", len(df_gold_full))\n",
+ "print(\"first m \", df_gold_full[\"Date\"].iloc[0])\n",
+ "print(\"last m \", df_gold_full[\"Date\"].iloc[-1])\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "71c49f6e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "month len: 513\n",
+ "first m : 1360/07\n",
+ "last m : 1403/03\n"
+ ]
+ }
+ ],
+ "source": [
+ "missing_stock = generate_missing_stock(\n",
+ " usd_df=df_usd,\n",
+ " start_date=\"1393/04\", \n",
+ " ratio=3.1\n",
+ ")\n",
+ "\n",
+ "\n",
+ "df_stock_full = pd.concat(\n",
+ " [missing_stock, df_stock],\n",
+ " ignore_index=True\n",
+ ")\n",
+ "\n",
+ "\n",
+ "df_stock_full = df_stock_full.sort_values(\"Date\").reset_index(drop=True)\n",
+ "\n",
+ "\n",
+ "print(\"month len:\", len(df_stock_full))\n",
+ "print(\"first m :\", df_stock_full[\"Date\"].iloc[0])\n",
+ "print(\"last m :\", df_stock_full[\"Date\"].iloc[-1])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "06fda698",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "month len 513\n",
+ "first m 1360/07\n",
+ "last m 1403/03\n"
+ ]
+ }
+ ],
+ "source": [
+ "missing_imami = generate_missing_imami(\n",
+ " usd_df=df_usd,\n",
+ " start_date=\"1389/01\", \n",
+ " ratio=383.4\n",
+ ")\n",
+ "\n",
+ "\n",
+ "df_imami_full = pd.concat(\n",
+ " [missing_imami, df_imami],\n",
+ " ignore_index=True\n",
+ ")\n",
+ "\n",
+ "\n",
+ "df_imami_full = df_imami_full.sort_values(\"Date\").reset_index(drop=True)\n",
+ "\n",
+ "\n",
+ "print(\"month len\", len(df_imami_full))\n",
+ "print(\"first m\", df_imami_full[\"Date\"].iloc[0])\n",
+ "print(\"last m\", df_imami_full[\"Date\"].iloc[-1])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "bd7279c2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Date Price\n",
+ "0 1360/07 0.00\n",
+ "1 1360/08 0.00\n",
+ "2 1360/09 0.00\n",
+ "3 1360/10 0.00\n",
+ "4 1360/11 0.00\n",
+ ".. ... ...\n",
+ "504 1402/07 90.03\n",
+ "505 1402/08 91.69\n",
+ "506 1402/09 92.01\n",
+ "507 1402/10 91.81\n",
+ "508 1402/11 97.34\n",
+ "\n",
+ "[509 rows x 2 columns]\n",
+ " Date Price\n",
+ "0 1360/07 0.00\n",
+ "1 1360/08 0.00\n",
+ "2 1360/09 0.00\n",
+ "3 1360/10 0.00\n",
+ "4 1360/11 0.00\n",
+ ".. ... ...\n",
+ "504 1402/07 90.51\n",
+ "505 1402/08 93.17\n",
+ "506 1402/09 95.31\n",
+ "507 1402/10 96.96\n",
+ "508 1402/11 98.67\n",
+ "\n",
+ "[509 rows x 2 columns]\n"
+ ]
+ }
+ ],
+ "source": [
+ "df_car = generate_car_index(df_usd)\n",
+ "df_housing = generate_housing_index(df_usd)\n",
+ "\n",
+ "print(df_car.head(-1))\n",
+ "print(df_housing.head(-1))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "8055eaff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_stock_full = df_stock_full.rename(columns={\"Price\": \"stock_price\"})\n",
+ "df_gold_full = df_gold_full.rename(columns={\"Price\": \"gold_price\"})\n",
+ "df_usd = df_usd.rename(columns={\"Price\": \"usd_price\"})\n",
+ "df_housing = df_housing.rename(columns={\"Price\": \"housing_price\"})\n",
+ "df_car = df_car.rename(columns={\"Price\": \"car_price\"})\n",
+ "df_imami_full = df_imami_full.rename(columns={\"Price\": \"imami_price\"})\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "18e4537c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "base_df = df_stock_full.copy()\n",
+ "\n",
+ "df_final = base_df.copy()\n",
+ "\n",
+ "df_final = df_final.merge(\n",
+ " df_gold_full,\n",
+ " on=\"Date\",\n",
+ " how=\"left\"\n",
+ ")\n",
+ "\n",
+ "df_final = df_final.merge(\n",
+ " df_usd,\n",
+ " on=\"Date\",\n",
+ " how=\"left\"\n",
+ ")\n",
+ "\n",
+ "df_final = df_final.merge(\n",
+ " df_housing,\n",
+ " on=\"Date\",\n",
+ " how=\"left\"\n",
+ ")\n",
+ "\n",
+ "df_final = df_final.merge(\n",
+ " df_car,\n",
+ " on=\"Date\",\n",
+ " how=\"left\"\n",
+ ")\n",
+ "\n",
+ "df_final = df_final.merge(\n",
+ " df_imami_full,\n",
+ " on=\"Date\",\n",
+ " how=\"left\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "1668be9d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_final = df_final.sort_values(\"Date\").reset_index(drop=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "341b6856",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_final = df_final.sort_values(\"Date\").reset_index(drop=True)\n",
+ "\n",
+ "df_final.ffill(inplace=True)\n",
+ "df_final.bfill(inplace=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "ea1700ec",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
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+ "
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "df_plot = df_final.copy()\n",
+ "cols = df_final.columns.drop(\"Date\")\n",
+ "\n",
+ "plt.figure(figsize=(16, 8))\n",
+ "\n",
+ "colors = {\n",
+ " \"stock_price\": \"#1f77b4\",\n",
+ " \"gold_price\": \"#ff7f0e\",\n",
+ " \"usd_price\": \"#2ca02c\",\n",
+ " \"housing_price\": \"#d62728\",\n",
+ " \"car_price\": \"#9467bd\",\n",
+ " \"imami_price\": \"#8c564b\"\n",
+ "}\n",
+ "\n",
+ "\n",
+ "for col in cols:\n",
+ " plt.plot(\n",
+ " range(len(df_plot)),\n",
+ " df_plot[col],\n",
+ " label=col.replace(\"_price\", \"\").capitalize(),\n",
+ " color=colors.get(col, \"black\"),\n",
+ " linewidth=2\n",
+ " )\n",
+ "\n",
+ "\n",
+ "xticks_idx = list(range(0, len(df_plot), 12))\n",
+ "xticks_labels = df_plot[\"Date\"].iloc[xticks_idx]\n",
+ "\n",
+ "plt.xticks(xticks_idx, xticks_labels, rotation=45)\n",
+ "plt.ylabel(\"Index (Base 100)\")\n",
+ "plt.title(\"30-Year Asset Comparison (Base-100)\", fontsize=16)\n",
+ "plt.grid(alpha=0.3)\n",
+ "plt.legend(loc=\"upper left\", fontsize=10)\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a267a538",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_final.to_csv(\"df_final.csv\", index=False, encoding=\"utf-8-sig\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d888b84b",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "base",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/AssetAnalysis/automotive/__pycache__/automotive.cpython-310.pyc b/AssetAnalysis/automotive/__pycache__/automotive.cpython-310.pyc
new file mode 100644
index 0000000..1672ff3
Binary files /dev/null and b/AssetAnalysis/automotive/__pycache__/automotive.cpython-310.pyc differ
diff --git a/AssetAnalysis/automotive/automotive.py b/AssetAnalysis/automotive/automotive.py
new file mode 100644
index 0000000..dc5f841
--- /dev/null
+++ b/AssetAnalysis/automotive/automotive.py
@@ -0,0 +1,29 @@
+import pandas as pd
+import numpy as np
+
+def generate_car_index(
+ usd_df: pd.DataFrame,
+ window: int = 3,
+ noise_std: float = 0.03,
+ seed: int = 42
+) -> pd.DataFrame:
+
+ np.random.seed(seed)
+
+ df = usd_df.copy()
+
+ smoothed = df["Price"].rolling(
+ window=window,
+ min_periods=1
+ ).mean()
+
+ noise = np.random.normal(0, noise_std, len(df))
+
+ raw_index = smoothed * (1 + noise)
+
+ # نرمالسازی 0–100
+ index_0_100 = 100 * (raw_index - raw_index.min()) / (raw_index.max() - raw_index.min())
+
+ df["Price"] = index_0_100.round(2)
+
+ return df[["Date", "Price"]]
diff --git a/AssetAnalysis/coin/Imami_coin.csv b/AssetAnalysis/coin/Imami_coin.csv
new file mode 100644
index 0000000..eb3a2e8
--- /dev/null
+++ b/AssetAnalysis/coin/Imami_coin.csv
@@ -0,0 +1,4337 @@
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diff --git a/AssetAnalysis/coin/__pycache__/gold_coin.cpython-310.pyc b/AssetAnalysis/coin/__pycache__/gold_coin.cpython-310.pyc
new file mode 100644
index 0000000..b2aa50f
Binary files /dev/null and b/AssetAnalysis/coin/__pycache__/gold_coin.cpython-310.pyc differ
diff --git a/AssetAnalysis/coin/gold_coin.py b/AssetAnalysis/coin/gold_coin.py
new file mode 100644
index 0000000..362181a
--- /dev/null
+++ b/AssetAnalysis/coin/gold_coin.py
@@ -0,0 +1,30 @@
+# coin/coin.py
+import pandas as pd
+import numpy as np
+
+def generate_missing_imami(
+ usd_df: pd.DataFrame,
+ start_date: str,
+ ratio: float,
+ noise_std: float = 0.02,
+ seed: int = 42
+) -> pd.DataFrame:
+
+ np.random.seed(seed)
+
+ missing_df = usd_df[usd_df["Date"] < start_date].copy()
+
+ if missing_df.empty:
+ return pd.DataFrame(columns=["Date", "Price"])
+
+ noise = np.random.normal(
+ loc=0,
+ scale=noise_std,
+ size=len(missing_df)
+ )
+
+ missing_df["Price"] = (
+ missing_df["Price"] * ratio * (1 + noise)
+ ).round()
+
+ return missing_df[["Date", "Price"]].reset_index(drop=True)
diff --git a/AssetAnalysis/common/utils.py b/AssetAnalysis/common/utils.py
new file mode 100644
index 0000000..2d341ca
--- /dev/null
+++ b/AssetAnalysis/common/utils.py
@@ -0,0 +1,42 @@
+import pandas as pd
+
+
+
+def daily_to_monthly(df: pd.DataFrame) -> pd.DataFrame:
+ if "Date" not in df.columns or "Price" not in df.columns:
+ raise ValueError("DataFrame must contain 'Date' and 'Price' columns.")
+
+ # Validate date format: mu|st be exactly 10 characters (YYYY/MM/DD)
+ date_lengths = df["Date"].astype(str).str.len()
+ if not (date_lengths == 10).all():
+ invalid_dates = df["Date"][date_lengths != 10].unique()
+ raise ValueError(
+ f"Invalid date format. Expected 'YYYY/MM/DD' (10 characters).\n"
+ f"Invalid examples: {invalid_dates[:5]}"
+ )
+
+ df_monthly = df.copy()
+ df_monthly["Date"] = df_monthly["Date"].str[:7]
+
+ result = (
+ df_monthly.groupby("Date")["Price"]
+ .mean()
+ .round()
+ .reset_index()
+ )
+
+ return result
+
+
+def calculate_change(df: pd.DataFrame) -> pd.DataFrame:
+
+ if "Price" not in df.columns:
+ raise ValueError("DataFrame must contain 'Price' column.")
+
+ df = df.copy()
+ df["change"] = df["Price"].pct_change().fillna(0.0).round(4)
+
+ return df
+
+
+
diff --git a/AssetAnalysis/df_final.csv b/AssetAnalysis/df_final.csv
new file mode 100644
index 0000000..c56b5aa
--- /dev/null
+++ b/AssetAnalysis/df_final.csv
@@ -0,0 +1,514 @@
+Date,stock_price,gold_price,usd_price,housing_price,car_price,imami_price
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diff --git a/AssetAnalysis/gold/24_carat_gold.csv b/AssetAnalysis/gold/24_carat_gold.csv
new file mode 100644
index 0000000..d50b5c8
--- /dev/null
+++ b/AssetAnalysis/gold/24_carat_gold.csv
@@ -0,0 +1,2706 @@
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diff --git a/AssetAnalysis/gold/__pycache__/gold.cpython-310.pyc b/AssetAnalysis/gold/__pycache__/gold.cpython-310.pyc
new file mode 100644
index 0000000..34625e9
Binary files /dev/null and b/AssetAnalysis/gold/__pycache__/gold.cpython-310.pyc differ
diff --git a/AssetAnalysis/gold/gold.py b/AssetAnalysis/gold/gold.py
new file mode 100644
index 0000000..ac66ad9
--- /dev/null
+++ b/AssetAnalysis/gold/gold.py
@@ -0,0 +1,23 @@
+# gold/gold.py
+import pandas as pd
+
+def generate_missing_gold(
+ usd_df: pd.DataFrame,
+ start_date: str,
+ ratio: float
+) -> pd.DataFrame:
+
+ missing_df = usd_df[usd_df["Date"] < start_date].copy()
+
+ if missing_df.empty:
+ return pd.DataFrame(columns=["Date", "Price"])
+
+ smoothed_usd = missing_df["Price"].rolling(
+ window=3,
+ center=True,
+ min_periods=1
+ ).mean()
+
+ missing_df["Price"] = (smoothed_usd * ratio).round()
+
+ return missing_df[["Date", "Price"]].reset_index(drop=True)
diff --git a/AssetAnalysis/main.py b/AssetAnalysis/main.py
new file mode 100644
index 0000000..4ee7ff2
--- /dev/null
+++ b/AssetAnalysis/main.py
@@ -0,0 +1,5 @@
+def main(): ...
+
+
+if __name__ == "__main__":
+ main()
diff --git a/AssetAnalysis/real_estate/__pycache__/real_estates.cpython-310.pyc b/AssetAnalysis/real_estate/__pycache__/real_estates.cpython-310.pyc
new file mode 100644
index 0000000..18771a9
Binary files /dev/null and b/AssetAnalysis/real_estate/__pycache__/real_estates.cpython-310.pyc differ
diff --git a/AssetAnalysis/real_estate/real_estates.py b/AssetAnalysis/real_estate/real_estates.py
new file mode 100644
index 0000000..b429068
--- /dev/null
+++ b/AssetAnalysis/real_estate/real_estates.py
@@ -0,0 +1,19 @@
+import pandas as pd
+
+def generate_housing_index(
+ usd_df: pd.DataFrame,
+ window: int = 12
+) -> pd.DataFrame:
+
+ df = usd_df.copy()
+
+ smoothed = df["Price"].rolling(
+ window=window,
+ min_periods=1
+ ).mean()
+
+ index_0_100 = 100 * (smoothed - smoothed.min()) / (smoothed.max() - smoothed.min())
+
+ df["Price"] = index_0_100.round(2)
+
+ return df[["Date", "Price"]]
diff --git a/AssetAnalysis/stock/__pycache__/stocks.cpython-310.pyc b/AssetAnalysis/stock/__pycache__/stocks.cpython-310.pyc
new file mode 100644
index 0000000..90da8b9
Binary files /dev/null and b/AssetAnalysis/stock/__pycache__/stocks.cpython-310.pyc differ
diff --git a/AssetAnalysis/stock/stock.csv b/AssetAnalysis/stock/stock.csv
new file mode 100644
index 0000000..11aae9f
--- /dev/null
+++ b/AssetAnalysis/stock/stock.csv
@@ -0,0 +1,2363 @@
+Date,Price
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diff --git a/AssetAnalysis/stock/stocks.py b/AssetAnalysis/stock/stocks.py
new file mode 100644
index 0000000..757b115
--- /dev/null
+++ b/AssetAnalysis/stock/stocks.py
@@ -0,0 +1,28 @@
+# stock/stock.py
+import pandas as pd
+import numpy as np
+
+def generate_missing_stock(
+ usd_df: pd.DataFrame,
+ start_date: str,
+ ratio: float,
+ gap_factor: float = 0.1,
+ gap_prob: float = 0.3,
+ seed: int = 42
+) -> pd.DataFrame:
+
+ np.random.seed(seed)
+
+ missing_df = usd_df[usd_df["Date"] < start_date].copy()
+
+ if missing_df.empty:
+ return pd.DataFrame(columns=["Date", "Price"])
+
+ gaps = np.random.rand(len(missing_df)) < gap_prob
+ gap_effect = np.where(gaps, gap_factor, 0)
+
+ missing_df["Price"] = (
+ missing_df["Price"] * ratio * (1 + gap_effect)
+ ).round()
+
+ return missing_df[["Date", "Price"]].reset_index(drop=True)
diff --git a/AssetAnalysis/usd/USD_IRR.csv b/AssetAnalysis/usd/USD_IRR.csv
new file mode 100644
index 0000000..22f3a5f
--- /dev/null
+++ b/AssetAnalysis/usd/USD_IRR.csv
@@ -0,0 +1,12721 @@
+Date,Price
+1360/07/07,270
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\ No newline at end of file
diff --git a/AssetAnalysis/usd/usd.py b/AssetAnalysis/usd/usd.py
new file mode 100644
index 0000000..e69de29