diff --git a/vega_query/visualisations/overlay.py b/vega_query/visualisations/overlay.py
index 9b299454..15c07a41 100644
--- a/vega_query/visualisations/overlay.py
+++ b/vega_query/visualisations/overlay.py
@@ -30,7 +30,7 @@ def overlay_mark_price(
     ax: Axes,
     market_data_history: List[protos.vega.vega.MarketData],
     price_decimals: int,
-):
+)-> pd.DataFrame:
     x = []
     y = []
     for market_data in market_data_history:
@@ -39,13 +39,19 @@ def overlay_mark_price(
         y.append(price if price != 0 else np.nan)
     ax.step(x, y, label="mark_price", where="post")
 
+    # Create a DataFrame from the extracted data
+    df_mark_price = pd.DataFrame({'timestamp': x, 'last_traded_price': y})
+    
+    # Return the DataFrame
+    return df_mark_price
+
 
 def overlay_last_traded_price(
     ax: Axes,
     market_data_history: List[protos.vega.vega.MarketData],
     price_decimals: int,
     **kwargs,
-):
+) :
     x = []
     y = []
     for market_data in market_data_history:
diff --git a/vega_query/visualisations/plots/amm.py b/vega_query/visualisations/plots/amm.py
index 506f03d6..4301864e 100644
--- a/vega_query/visualisations/plots/amm.py
+++ b/vega_query/visualisations/plots/amm.py
@@ -77,11 +77,10 @@ def create(
     axn0l = fig.add_subplot(gs[0, 0])
     axn0r: Axes = axn0l.twinx()
     if market_data_history is not None:
-        overlay_mark_price(axn0l, market_data_history, market.decimal_places)
+        df_markPrice=overlay_mark_price(axn0l, market_data_history, market.decimal_places)
+        df_markPrice.to_csv("mark_price_data.csv", index=False)
         overlay_last_traded_price(axn0l, market_data_history, market.decimal_places)
         overlay_trading_mode(axn0r, market_data_history)
-        # overlay_auction_starts(axn0r, market_data_history)
-        # overlay_auction_ends(axn0r, market_data_history)
 
     axn0l.set_ylabel("USDT")
     axn0l.set_title(
@@ -155,6 +154,8 @@ def create(
             asset_decimals=asset.details.decimals,
         )
         ax21.step(df.index, df.total, where="post", label="AMM portfolio")
+        df.to_csv("AMM_Portfolio_data.csv", index=False)
+
 
     ax11.legend()
     ax21.legend()
diff --git a/vega_sim/scenario/common/utils/price_process.py b/vega_sim/scenario/common/utils/price_process.py
index 39e72b29..aec39437 100644
--- a/vega_sim/scenario/common/utils/price_process.py
+++ b/vega_sim/scenario/common/utils/price_process.py
@@ -62,7 +62,7 @@ def random_walk(
             S = np.round(S, decimal_precision)
 
         # If random state is passed then error if it generates a negative price
-        # Otherwise retry with a new seed
+        # Otherwise retry with a new 
 
         if trim_to_min is not None:
             S[S < trim_to_min] = trim_to_min
@@ -315,7 +315,7 @@ def get_live_price(
         return _live_prices[feed_key]
 
 
-def ou_price_process(n, theta=0.15, mu=0.0, sigma=0.2, x0=1.0, drift=0.0):
+def ou_price_process(n, theta=0.15, mu=0.0, sigma=0.2, x0=1.0, drift=0.0, seed=42):
     """
     Generates a mean-reverting price series using the Ornstein–Uhlenbeck
     process with an optional drift term.
@@ -331,6 +331,8 @@ def ou_price_process(n, theta=0.15, mu=0.0, sigma=0.2, x0=1.0, drift=0.0):
     Returns:
         np.ndarray: Mean-reverting price series of length n.
     """
+    if seed is not None:
+        np.random.seed(seed)  # Set the random seed for reproducibility
     dt = 1.0  # Assuming a time step of 1
     x = np.zeros(n)
     x[0] = x0