docs: update the tutorials.

nkaz001 · Aug 10, 2024 · b6e591d · b6e591d
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Showing 23 changed files with 5,545 additions and 3,269 deletions.
diff --git a/docs/tutorials/Data Preparation.ipynb b/docs/tutorials/Data Preparation.ipynb
diff --git a/docs/tutorials/GLFT Market Making Model and Grid Trading.ipynb b/docs/tutorials/GLFT Market Making Model and Grid Trading.ipynb
diff --git a/docs/tutorials/Getting Started.ipynb b/docs/tutorials/Getting Started.ipynb
diff --git a/docs/tutorials/High-Frequency Grid Trading.ipynb b/docs/tutorials/High-Frequency Grid Trading.ipynb
diff --git a/docs/tutorials/Impact of Order Latency.ipynb b/docs/tutorials/Impact of Order Latency.ipynb
@@ -120,9 +120,9 @@
     "                trade_price_tick = last_trade.px / tick_size\n",
     "                \n",
     "                if last_trade.ev & BUY_EVENT == BUY_EVENT:\n",
-    "                    depth = np.nanmax([trade_price_tick - mid_price_tick, depth])\n",
+    "                    depth = max(trade_price_tick - mid_price_tick, depth)\n",
     "                else:\n",
-    "                    depth = np.nanmax([mid_price_tick - trade_price_tick, depth])\n",
+    "                    depth = max(mid_price_tick - trade_price_tick, depth)\n",
     "            arrival_depth[t] = depth\n",
     "        \n",
     "        hbt.clear_last_trades(asset_no)\n",
@@ -151,12 +151,13 @@
     "                # Calibrates A, k\n",
     "                tmp[:] = 0\n",
     "                lambda_ = measure_trading_intensity(arrival_depth[t + 1 - 6_000:t + 1], tmp)\n",
-    "                lambda_ = lambda_[:70] / 600\n",
-    "                x = ticks[:len(lambda_)]\n",
-    "                y = np.log(lambda_)\n",
-    "                k_, logA = linear_regression(x, y)\n",
-    "                A = np.exp(logA)\n",
-    "                k = -k_\n",
+    "                if len(lambda_) > 2:\n",
+    "                    lambda_ = lambda_[:70] / 600\n",
+    "                    x = ticks[:len(lambda_)]\n",
+    "                    y = np.log(lambda_)\n",
+    "                    k_, logA = linear_regression(x, y)\n",
+    "                    A = np.exp(logA)\n",
+    "                    k = -k_\n",
     "           \n",
     "                # Updates the volatility.\n",
     "                volatility = np.nanstd(mid_price_chg[t + 1 - 6_000:t + 1]) * np.sqrt(10)\n",
@@ -171,8 +172,8 @@
     "        \n",
     "        reservation_price_tick = mid_price_tick - skew * position\n",
     "\n",
-    "        bid_price_tick = np.minimum(np.round(reservation_price_tick - half_spread_tick), best_bid_tick)\n",
-    "        ask_price_tick = np.maximum(np.round(reservation_price_tick + half_spread_tick), best_ask_tick)\n",
+    "        bid_price_tick = min(np.round(reservation_price_tick - half_spread_tick), best_bid_tick)\n",
+    "        ask_price_tick = max(np.round(reservation_price_tick + half_spread_tick), best_ask_tick)\n",
     "        \n",
     "        bid_price = bid_price_tick * tick_size\n",
     "        ask_price = ask_price_tick * tick_size\n",

diff --git a/docs/tutorials/Integrating Custom Data.ipynb b/docs/tutorials/Integrating Custom Data.ipynb
diff --git a/docs/tutorials/Making Multiple Markets.ipynb b/docs/tutorials/Making Multiple Markets.ipynb
diff --git a/docs/tutorials/Probability Queue Models.ipynb b/docs/tutorials/Probability Queue Models.ipynb
diff --git a/docs/tutorials/Working with Market Depth and Trades.ipynb b/docs/tutorials/Working with Market Depth and Trades.ipynb
diff --git a/examples/Data Preparation.ipynb b/examples/Data Preparation.ipynb
diff --git a/examples/GLFT Market Making Model and Grid Trading.ipynb b/examples/GLFT Market Making Model and Grid Trading.ipynb
diff --git a/examples/Getting Started.ipynb b/examples/Getting Started.ipynb
diff --git a/examples/High-Frequency Grid Trading.ipynb b/examples/High-Frequency Grid Trading.ipynb
diff --git a/examples/Impact of Order Latency.ipynb b/examples/Impact of Order Latency.ipynb
@@ -120,9 +120,9 @@
     "                trade_price_tick = last_trade.px / tick_size\n",
     "                \n",
     "                if last_trade.ev & BUY_EVENT == BUY_EVENT:\n",
-    "                    depth = np.nanmax([trade_price_tick - mid_price_tick, depth])\n",
+    "                    depth = max(trade_price_tick - mid_price_tick, depth)\n",
     "                else:\n",
-    "                    depth = np.nanmax([mid_price_tick - trade_price_tick, depth])\n",
+    "                    depth = max(mid_price_tick - trade_price_tick, depth)\n",
     "            arrival_depth[t] = depth\n",
     "        \n",
     "        hbt.clear_last_trades(asset_no)\n",
@@ -151,12 +151,13 @@
     "                # Calibrates A, k\n",
     "                tmp[:] = 0\n",
     "                lambda_ = measure_trading_intensity(arrival_depth[t + 1 - 6_000:t + 1], tmp)\n",
-    "                lambda_ = lambda_[:70] / 600\n",
-    "                x = ticks[:len(lambda_)]\n",
-    "                y = np.log(lambda_)\n",
-    "                k_, logA = linear_regression(x, y)\n",
-    "                A = np.exp(logA)\n",
-    "                k = -k_\n",
+    "                if len(lambda_) > 2:\n",
+    "                    lambda_ = lambda_[:70] / 600\n",
+    "                    x = ticks[:len(lambda_)]\n",
+    "                    y = np.log(lambda_)\n",
+    "                    k_, logA = linear_regression(x, y)\n",
+    "                    A = np.exp(logA)\n",
+    "                    k = -k_\n",
     "           \n",
     "                # Updates the volatility.\n",
     "                volatility = np.nanstd(mid_price_chg[t + 1 - 6_000:t + 1]) * np.sqrt(10)\n",
@@ -171,8 +172,8 @@
     "        \n",
     "        reservation_price_tick = mid_price_tick - skew * position\n",
     "\n",
-    "        bid_price_tick = np.minimum(np.round(reservation_price_tick - half_spread_tick), best_bid_tick)\n",
-    "        ask_price_tick = np.maximum(np.round(reservation_price_tick + half_spread_tick), best_ask_tick)\n",
+    "        bid_price_tick = min(np.round(reservation_price_tick - half_spread_tick), best_bid_tick)\n",
+    "        ask_price_tick = max(np.round(reservation_price_tick + half_spread_tick), best_ask_tick)\n",
     "        \n",
     "        bid_price = bid_price_tick * tick_size\n",
     "        ask_price = ask_price_tick * tick_size\n",