Create a Market Profile Dashboard Using Python

pip install streamlit plotly pandas numpy python-dateutil tradermade 

import streamlit as st import plotly.graph_objects as go import pandas as pd import numpy as np import datetime from dateutil.relativedelta import relativedelta import streamlit.components.v1 as components import tradermade as tm tm.set_rest_api_key('Your_API_Key') # Replace with your TraderMade API Key 

def get_data_raw(currency_pair, start_time, end_time, interval='minute'): try: # Fetch data using TraderMade SDK data = tm.timeseries( currency=currency_pair, start=start_time, end=end_time, interval=interval, period=30, fields=["open", "high", "low", "close"] ) # print(data) # Print the response to see its structure # print("API Response:", data) # Convert the response directly into a DataFrame df = pd.DataFrame(data) df['volume'] = df['close'] # Convert the 'date' column to datetime if it exists df = df.replace(0, np.nan) df = df.dropna() df.set_index("date", inplace=True) df = np.round(df, 4) if 'date' in df.columns: df['date'] = pd.to_datetime(df['date']) df.set_index('date', inplace=True) # Sort the DataFrame by index df.sort_index(inplace=True) return df except KeyError as e: print(f"KeyError: {e}") return pd.DataFrame() # Return an empty DataFrame in case of an error except Exception as e: print(f"An error occurred: {e}") return pd.DataFrame() # Return an empty DataFrame in case of a general error def get_data_MP(g, p, f, F, cur, st, ed): df = get_data_raw(cur, st, ed) return df 

def last_work_date(date, format): if datetime.datetime.strptime(date, "%Y-%m-%d").weekday() == 6: return (datetime.datetime.strptime(date, "%Y-%m-%d")-relativedelta(days=2)).strftime(format) elif datetime.datetime.strptime(date, "%Y-%m-%d").weekday() == 0: return (datetime.datetime.strptime(date, "%Y-%m-%d")-relativedelta(days=3)).strftime(format) else: return (datetime.datetime.strptime(date, "%Y-%m-%d")-relativedelta(days=1)).strftime(format) 

def get_rd(currency): od_list = ["UKOIL", "OIL", "XAGUSD","EVAUST","LUNUST",'LTCUST','XMRUST'] od2_list = ["JPY"] od3_list = ["AAPL", "AMZN", "NFLX", "TSLA", "GOOGL", "BABA", "TWTR", "BAC", "BIDU", 'XAUUSD','SOLUST','BNBUST','BCHUST','DSHUST','EGLUST'] od4_list = ["UK100", "SPX500","FRA40", "GER30","JPN225","NAS100","USA30", "HKG33", "AUS200","BTC","BTCUST",'ETHUSD',"ETHUST"] cfd_list = ["EURNOK", "EURSEK", "USDSEK","USDNOK","NEOUST","ETCUST","DOTUST", "UNIUST"] if currency in od_list or currency[3:6] in od2_list: ad = .01 rd = 2 elif currency in od3_list: ad = 0.1 rd = 1 elif currency in od4_list: ad = 2 rd = 0 elif currency in cfd_list: ad = .001 rd = 3 else: ad = 0.0001 rd = 4 return rd, ad def get_ad(ad, max, min): if (max-min) > ad*2000: return ad*50 if (max-min) > ad*1000: return ad*20 if (max-min) > ad*300: return ad*5 elif (max-min) > ad*100: return ad*2 else: return ad 

def midmax_idx(array): if len(array) == 0: return None # Find candidate maxima # maxima_idxs = np.argwhere(array.to_numpy() == np.amax(array.to_numpy()))[:, 0] maxima_idxs = np.argwhere(array == np.amax(array))[:,0] if len(maxima_idxs) == 1: return maxima_idxs[0] elif len(maxima_idxs) <= 1: return None # Find the distances from the midpoint to find # the maxima with the least distance midpoint = len(array) / 2 v_norm = np.vectorize(np.linalg.norm) maximum_idx = np.argmin(v_norm(maxima_idxs - midpoint)) return maxima_idxs[maximum_idx] def calculate_value_area(poc_volume, target_vol, poc_idx, mp): min_idx = poc_idx max_idx = poc_idx while poc_volume < target_vol: last_min = min_idx last_max = max_idx next_min_idx = np.clip(min_idx - 1, 0, len(mp) - 1) next_max_idx = np.clip(max_idx + 1, 0, len(mp) - 1) low_volume = mp.iloc[next_min_idx].vol if next_min_idx != last_min else None high_volume = mp.iloc[next_max_idx].vol if next_max_idx != last_max else None if not high_volume or (low_volume and low_volume > high_volume): poc_volume += low_volume min_idx = next_min_idx elif not low_volume or (high_volume and low_volume <= high_volume): poc_volume += high_volume max_idx = next_max_idx else: break return mp.iloc[min_idx].value, mp.iloc[max_idx].value mp_dict = {"0000":"A","0030":"B","0100":"C","0130":"D","0200":"E","0230":"F","0300":"G","0330":"H","0400":"I","0430":"J","0500":"K","0530":"L","0600":"M","0630":"N","0700":"O","0730":"P","0800":"Q","0830":"R","0900":"S","0930":"T","1000":"U","1030":"V","1100":"W","1130":"X","1200":"a","1230":"b","1300":"c","1330":"d","1400":"e","1430":"f","1500":"g","1530":"h","1600":"i","1630":"j","1700":"k","1730":"l","1800":"m","1830":"n","1900":"o","1930":"p","2000":"q","2030":"r","2100":"s","2130":"t","2200":"u","2230":"v","2300":"w","2330":"x",} 

def cal_mar_pro(currency, study, freq, period, mode, fp, mp_st, mp_ed, date): st = datetime.datetime.strptime(mp_st, "%Y-%m-%d-%H:%M") print(currency, mp_st, mp_ed) # try: rf = get_data_MP("M", str(freq), "%Y-%m-%d-%H:%M" , "%Y-%m-%d-%H:%M",currency, mp_st, mp_ed) hf = rf.copy() last_price = rf.iloc[-1]["close"] rf = rf[["high","low"]] rf.index = pd.to_datetime(rf.index) #rf = rf[:96] rd, ad = get_rd(currency) max = round(rf.high.max(), rd) min = round(rf.low.min(), rd) ad = get_ad(ad, max, min) try: x_data = np.round(np.arange(min, max, ad).tolist(), rd) except: print("MP loop failed", currency) # x_data = x_data[::-1] y_data= [] z_data = [] y_data1= [] z_data1 = [] tocount1 = 0 for item in x_data: alpha = "" alpha1 = "" alpha2 = "" for i in range(len(rf)): if rf.index[i] < date: if round(rf.iloc[i]["high"], rd) >= item >= round(rf.iloc[i]["low"], rd): alpha += mp_dict[rf.index[i].strftime("%H%M")] elif rf.index[i] >= date: if round(rf.iloc[i]["high"], rd) >= item >= round(rf.iloc[i]["low"], rd): alpha1 += mp_dict[rf.index[i].strftime("%H%M")] tocount1 += len(alpha1) y_data.append(len(alpha)) y_data1.append(len(alpha1)) z_data.append(alpha) z_data1.append(alpha1) # y_data = y_data[::-1] # y_data1 = y_data1[::-1] mp = pd.DataFrame([x_data, y_data1]).T mp = mp[::-1] mp = mp.rename(columns={0:"value",1:"vol"}) #poc_idx = midmax_idx(mp.vol) poc_idx = midmax_idx(mp.vol.values) poc_vol = mp.iloc[poc_idx].vol poc_price = mp.iloc[poc_idx].value target_vol = 0.7*tocount1 value_high,value_low = calculate_value_area(poc_vol, target_vol, poc_idx, mp) print("Value area",tocount1, 0.7*tocount1) return x_data, y_data,y_data1, z_data, z_data1, value_high, value_low, poc_price, last_price, hf 

# Streamlit code to take input st.set_page_config(layout="wide") st.title("Market Profile Dashboard") # currency = st.sidebar.text_input("Enter Currency Pair (e.g., EURUSD):", "EURUSD") category = st.sidebar.selectbox("Select Category", ["CFD", "Forex"], index=["CFD", "Forex"].index(st.session_state.category)) item_list = tm.cfd_list() if category == "CFD" else ["EURUSD", "GBPUSD", "AUDUSD", "USDCAD", "EURNOK", "USDJPY", "EURGBP", "BTCUSD", "USDCHF", "NZDUSD", "USDINR", "USDZAR", "ETHUSD", "EURSEK"] # Ensure item_list is a list and contains the default currency if isinstance(item_list, list) and st.session_state.currency in item_list: index = item_list.index(st.session_state.currency) else: index = 0 # Default to the first item if the currency is not in the list currency = st.sidebar.selectbox("Select an Item", item_list, index=index) study = st.sidebar.selectbox("Select Study Type:", ["MP"]) date = st.sidebar.date_input("Select Date") # Subtract one day using relativedelta to get the previous day at 00:00 hours mp_st = last_work_date(date.strftime('%Y-%m-%d'), '%Y-%m-%d-00:00') if date != datetime.datetime.now().date(): mp_ed = date.strftime('%Y-%m-%d-23:59') else: mp_ed = datetime.datetime.now().strftime('%Y-%m-%d-%H:%M') # Convert to string format # mp_st = previous_day.strftime("%Y-%m-%d-%H:%M") freq = st.sidebar.selectbox("Select Minutes per period:", [30]) period = st.sidebar.selectbox("Select periods:", [24]) mode = st.sidebar.selectbox("Select Mode:", ["tpo"]) fp = int((60/int(freq))*period) if date.weekday() in [5, 6]: # 5 = Saturday, 6 = Sunday st.warning("Please select a weekday (Monday to Friday). Weekends are not allowed.") st.stop() # Stop the app execution if the date is a weekend # Check if the button has been clicked using session state if "button_clicked" not in st.session_state: st.session_state.button_clicked = False # Create the Market Profile chart only when the button is clicked if st.sidebar.button("Get Market Profile"): st.session_state.button_clicked = True 

if st.session_state.button_clicked: #Get Market profile x_data, y_data,y_data1, z_data, z_data1, value_high, value_low, poc_price, last_price, hf = cal_mar_pro(currency, study, freq, period, mode, fp, mp_st,mp_ed, date) # Separate data for the previous day and current day previous_day_prices = x_data current_day_prices = x_data previous_day_counts = y_data current_day_counts = y_data1 # Create the Market Profile chart for previous day candlestick = go.Candlestick( x=hf.index, open=hf['open'], high=hf['high'], low=hf['low'], close=hf['close'], name=f'{currency} Candlestick Chart', ) tpo_dict = {} # Map index to date, using '00:00' hours formatting if necessary for i in range(len(hf.index)): tpo_dict[i] = hf.index[i] previous_day_dates = [] current_day_dates = [] for i in previous_day_counts: previous_day_dates.append(tpo_dict[i]) for i in current_day_counts: current_day_dates.append(tpo_dict[i]) # Create a vertical bar chart using the same x-axis (dates) and y-axis (volume) bar_chart = go.Bar( x=previous_day_dates, # Use the same dates for x-axis y=previous_day_prices, # Volume data for y-axis orientation='h', name="TPO Count previous day", marker=dict( color='rgba(50, 171, 96, 0.6)', line=dict(color='rgba(50, 171, 96, 1.0)', width=1) ), opacity=0.5, # Adjust opacity to make both charts visible hoverinfo='skip' ) bar_chart2 = go.Bar( x=current_day_dates, # Use the same dates for x-axis y=current_day_prices, # Volume data for y-axis orientation='h', name=f"TPO Count {date}", marker=dict( color='rgba(100, 149, 237, 0.6)', line=dict(color='rgba(100, 149, 237, 1.0)', width=1), ), opacity=0.5, # Adjust opacity to make both charts visible hoverinfo='skip' ) # Initialize the figure and add both traces fig1 = go.Figure() fig1.add_trace(candlestick) fig1.add_trace(bar_chart) fig1.add_trace(bar_chart2) print(y_data[0],y_data[-1:], poc_price) num_ticks = 6 # Number of ticks to display tick_indices = np.linspace(0, len(hf.index) - 1, num_ticks, dtype=int) tickvals = [hf.index[i] for i in tick_indices] ticktext = [hf.index[i] for i in tick_indices] # Update x-axis to show fewer date labels fig1.update_xaxes( tickvals=tickvals, # Specify which dates to show ticktext=ticktext, # Specify the labels for those dates tickangle=0, # Keep the tick labels horizontal title_text='Date', showgrid=True, ) fig1.update_yaxes( showgrid=True, ) # Update layout for the combined chart fig1.update_layout( title=f'{currency} Candlestick Chart with TPO Bars', xaxis_title='Date/TPO Count', yaxis_title='Price', yaxis=dict(range=[x_data[0], x_data[-1]]), # Reverse the y-axis height=600, xaxis=dict( type='category', # Use 'category' type to skip missing dates categoryorder='category ascending', # Order categories in ascending order showgrid=True, ), xaxis_rangeslider_visible=False # Hide the range slider ) # Create the Market Profile chart for current day fig2 = go.Figure() fig2.add_trace(go.Bar( x=previous_day_counts, # Count on the x-axis y=previous_day_prices, # Price levels on the y-axis orientation='h', name=f"TPO Count Previous day", marker=dict( color='rgba(50, 171, 96, 0.6)', line=dict(color='rgba(50, 171, 96, 1.0)', width=1) ), opacity=0.5 )) fig2.add_trace(go.Bar( x=current_day_counts, # Count on the x-axis y=current_day_prices, # Price levels on the y-axis orientation='h', name=f"TPO Count {date}", marker=dict( color='rgba(100, 149, 237, 0.6)', line=dict(color='rgba(100, 149, 237, 1.0)', width=1), ), )) fig2.update_xaxes( showgrid=True, ) fig2.update_yaxes( showgrid=True, ) fig2.update_layout( title=f"{currency} Two-day Market Profile", xaxis_title="TPO Count", yaxis_title="Price Levels", template="plotly_white", height=600, width=600 ) # Create two columns to display charts side by side col1, col2 = st.columns([1, 1]) with col1: st.plotly_chart(fig1, use_container_width=True) # Adjusts the chart to fit the column width with col2: st.plotly_chart(fig2, use_container_width=True) style = """ <style> table { width: 100%; border-collapse: collapse; font-family: 'Courier New', Courier, monospace; /* Monospaced font */ } th, td { border: 1px solid #ddd; padding: 8px; text-align: left; white-space: nowrap; /* Ensure no text wrapping */ font-weight: bold; } th { background-color: #f2f2f2; font-weight: bold; } tr:nth-child(even) { background-color: #f9f9f9; /* Light gray background for even rows */ } tr:hover { background-color: #f1f1f1; /* Highlight row on hover */ } </style>""" html_table = style html_table += "<table style='width:100%; border: 1px solid black; font-size: 12px; border-collapse: collapse;font-family: 'Courier New', Courier, monospace;'>" html_table += "<tr><th style='border: 1px solid black; padding: 2px;'>Prices</th>" html_table += "<th style='border: 1px solid black;padding: 2px;'>Previous Day</th>" html_table += f"<th style='border: 1px solid black;padding: 2px;'>{date}</th></tr>" # Populate the table rows for x, z, z1 in zip(reversed(x_data), reversed(z_data), reversed(z_data1)): if x in [value_low, poc_price, value_high]: html_table += f"<tr><td style='color:#FF6961; border: 1px solid black; padding: 0px;'>{x}</td>" html_table += f"<td style='color:#FF6961; border: 1px solid black; padding: 0px;'>{z}</td>" html_table += f"<td style='color:#FF6961; border: 1px solid black; padding: 0px;'>{z1}</td></tr>" else: html_table += f"<tr><td style='border: 1px solid black; padding: 0px;'>{x}</td>" html_table += f"<td style='border: 1px solid black; padding: 0px;'>{z}</td>" html_table += f"<td style='border: 1px solid black; padding: 0px;'>{z1}</td></tr>" html_table += "</table>" html_table2 = style html_table2 += f""" <table style='width:100%; border: 1px solid black; border-collapse: collapse; font-size: 10px;'> <tr> <th style='border: 1px solid black; padding: 2px;'>Letter</th> <th style='border: 1px solid black; padding: 2px;'>Period</th> </tr> """ # Populate the second table rows in reverse order or different formatting for letter, period in mp_dict.items(): html_table2 += f""" <tr> <td style='border: 1px solid black; padding: 2px;'>{letter}</td> <td style='border: 1px solid black; padding: 2px;'>{period}</td> </tr> """ html_table2 += f"</table>" html_table3 = style html_table3 += f""" <table style='width:100%; border: 1px solid black; border-collapse: collapse; font-size: 10px;'> <tr> <th style='border: 1px solid black; padding: 2px;'>Key Prices</th> <th style='border: 1px solid black; padding: 2px;'>Values</th> </tr> """ html_table3 += f""" <tr> <td style='border: 1px solid black; padding: 2px;'>Value Area High</td> <td style='border: 1px solid black; padding: 2px;'>{value_high}</td> </tr> <tr> <td style='border: 1px solid black; padding: 2px;'>Point of Control (POC)</td> <td style='border: 1px solid black; padding: 2px;'>{poc_price}</td> </tr> <tr> <td style='border: 1px solid black; padding: 2px;'>Value Area Low</td> <td style='border: 1px solid black; padding: 2px;'>{value_low}</td> </tr> </table> """ col3, col4 = st.columns([2, 2]) with col1: components.html(html_table, height=2000, scrolling=True) with col2: components.html(html_table3,height=90, scrolling=True) components.html(html_table2, height=1200, scrolling=True)