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Place-Order-Trading-Bot/placedOrderBot.ipynb
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Setup

Import Libaries

In [172]:
import pandas as pd
import MetaTrader5 as mt
import time
import re

Login

In [173]:
# login to your Trading Account - sign up in the description
mt.initialize()
    
login = 10730196
password = '#mDJu9O3'
server = 'VantageInternational-Demo'

mt.login(login, password, server)
Out [173]:
True

Set Symbol

In [174]:
symbol = 'XAUUSD'

Calculate

Get Alltimehigh

In [267]:
ohlc = mt.copy_rates_from_pos(symbol, mt.TIMEFRAME_D1, 0, 60)
ohlc_df = pd.DataFrame(ohlc)
ohlc_df['time']=pd.to_datetime(ohlc_df['time'], unit='s')
ohlc_df
Out [267]:
time open high low close tick_volume spread real_volume
0 2025-01-31 2796.26 2817.07 2791.03 2799.25 176088 18 0
1 2025-02-03 2800.67 2830.72 2772.09 2814.76 222780 18 0
2 2025-02-04 2814.23 2845.34 2807.33 2842.09 190207 18 0
3 2025-02-05 2842.57 2882.22 2839.76 2867.22 144079 18 0
4 2025-02-06 2865.36 2873.34 2834.23 2855.99 136634 18 0
5 2025-02-07 2855.96 2886.76 2852.55 2861.36 174549 18 0
6 2025-02-10 2858.80 2911.71 2854.73 2907.97 174533 18 0
7 2025-02-11 2907.84 2942.67 2881.61 2897.68 221876 18 0
8 2025-02-12 2898.02 2909.09 2864.20 2904.11 185547 18 0
9 2025-02-13 2904.02 2929.41 2900.54 2928.40 152063 18 0
10 2025-02-14 2927.85 2939.99 2876.94 2882.55 180610 18 0
11 2025-02-17 2884.84 2906.38 2878.83 2897.46 129712 18 0
12 2025-02-18 2898.40 2936.88 2891.99 2935.23 139030 18 0
13 2025-02-19 2935.71 2946.87 2918.54 2932.76 160944 18 0
14 2025-02-20 2933.47 2954.83 2924.08 2938.61 155405 18 0
15 2025-02-21 2938.70 2949.74 2916.75 2935.95 159274 18 0
16 2025-02-24 2935.19 2956.22 2921.30 2952.46 171870 18 0
17 2025-02-25 2952.42 2953.75 2888.18 2914.93 198402 18 0
18 2025-02-26 2915.06 2930.16 2890.77 2916.36 158179 18 0
19 2025-02-27 2916.73 2920.82 2867.77 2877.15 207221 18 0
20 2025-02-28 2877.31 2885.07 2832.62 2859.03 224045 18 0
21 2025-03-03 2856.18 2895.21 2855.57 2893.66 205740 18 0
22 2025-03-04 2892.77 2927.81 2881.92 2917.65 214258 18 0
23 2025-03-05 2917.84 2929.88 2894.34 2919.11 231043 18 0
24 2025-03-06 2919.52 2926.51 2891.20 2911.01 219058 18 0
25 2025-03-07 2911.39 2930.42 2896.78 2912.06 225312 18 0
26 2025-03-10 2911.25 2918.22 2880.22 2889.02 220535 18 0
27 2025-03-11 2888.82 2922.13 2880.30 2916.08 175258 18 0
28 2025-03-12 2915.66 2940.48 2906.11 2933.24 170540 18 0
29 2025-03-13 2934.10 2989.05 2932.89 2989.01 177969 18 0
30 2025-03-14 2989.31 3004.93 2978.46 2986.39 197059 18 0
31 2025-03-17 2984.83 3001.86 2982.13 3001.42 155946 18 0
32 2025-03-18 3001.16 3038.25 2999.39 3034.08 179165 18 0
33 2025-03-19 3033.89 3051.96 3022.79 3047.26 174636 18 0
34 2025-03-20 3047.96 3057.46 3025.69 3044.64 200333 18 0
35 2025-03-21 3044.55 3047.43 2999.46 3023.15 185542 18 0
36 2025-03-24 3021.99 3033.33 3002.46 3011.81 193224 18 0
37 2025-03-25 3011.62 3035.94 3007.55 3020.34 153347 18 0
38 2025-03-26 3019.39 3032.03 3012.34 3018.96 162718 18 0
39 2025-03-27 3019.37 3059.67 3017.60 3056.73 196828 18 0
40 2025-03-28 3056.57 3086.76 3054.19 3084.56 187584 18 0
41 2025-03-31 3086.34 3127.96 3076.77 3124.07 209942 18 0
42 2025-04-01 3122.98 3148.96 3100.79 3114.26 206537 18 0
43 2025-04-02 3113.80 3143.32 3105.20 3133.56 188846 18 0
44 2025-04-03 3142.25 3167.64 3054.21 3113.32 313074 0 0
45 2025-04-04 3114.54 3136.61 3015.79 3038.42 252778 18 0
46 2025-04-07 3008.62 3055.38 2956.52 2982.60 212668 0 0
47 2025-04-08 2983.16 3022.64 2974.72 2982.34 185173 18 0
48 2025-04-09 2983.48 3099.46 2969.98 3082.91 253727 18 0
49 2025-04-10 3083.53 3176.29 3071.39 3175.77 184496 18 0
50 2025-04-11 3175.72 3245.34 3175.71 3237.59 187568 18 0
51 2025-04-14 3226.90 3245.72 3193.72 3210.36 157467 18 0
52 2025-04-15 3211.18 3233.55 3210.02 3229.79 115741 18 0
53 2025-04-16 3231.04 3342.39 3229.80 3342.26 172558 18 0
54 2025-04-17 3343.13 3357.71 3283.98 3327.48 162159 18 0
55 2025-04-21 3332.68 3430.48 3328.90 3424.67 165503 18 0
56 2025-04-22 3424.07 3500.04 3366.85 3381.28 205173 18 0
57 2025-04-23 3353.28 3386.63 3260.21 3288.34 193560 18 0
58 2025-04-24 3288.51 3367.41 3288.45 3349.38 160771 18 0
59 2025-04-25 3350.42 3370.71 3265.04 3318.69 240555 18 0
In [268]:
# alltime high, low - last 60 days
ath_df = ohlc_df.iloc[ohlc_df['high'].idxmax()]
ath = round(ath_df['high'],0)
atl = round(ath_df['low'],0)
ath, atl
Out [268]:
(3500.0, 3367.0)
In [269]:
#lastday high, low
ldh = ohlc_df.iloc[-2]['high']
ldl = ohlc_df.iloc[-2]['low']
ldh, ldl
Out [269]:
(3367.41, 3288.45)
In [270]:
#today high, low
tdh = ohlc_df.iloc[-1]['high']
tdl = ohlc_df.iloc[-1]['low']
tdh ,tdl
Out [270]:
(3370.71, 3265.04)

Get High and Low last 5 Minutes intervall - 75 candles

In [271]:
def tp_sl():
    ohlc = mt.copy_rates_from_pos('XAUUSD', mt.TIMEFRAME_M5, 1, 75)
    ohlc_df = pd.DataFrame(ohlc)
    ohlc_df['time']=pd.to_datetime(ohlc_df['time'], unit='s')


    # Create a column to mark the start of 5-minute intervals
    ohlc_df['box_start'] = (ohlc_df['time'].dt.minute % 15 == 0).astype(int)
    # Create columns for rolling maximum and minimum over the last 5 candles, excluding the current candle
    ohlc_df['max_box'] = ohlc_df['high'].shift(1).rolling(window=15).max()
    ohlc_df['min_box'] = ohlc_df['low'].shift(1).rolling(window=15).min()
    # Breaking signal
    #ohlc_df["Break_signal"] =  (ohlc_df["close"] > ohlc_df["max_box"]).astype(int) * 2 + (ohlc_df["close"] < ohlc_df["min_box"]).astype(int)
    #print(ohlc_df['max_box'][14].astype(int)*2)

    return ohlc_df
In [272]:
lastchart = tp_sl()
lastchart = lastchart.drop(lastchart[lastchart.box_start != 1].index)
lastchart
Out [272]:
time open high low close tick_volume spread real_volume box_start max_box min_box
1 2025-04-25 17:45:00 3282.20 3282.66 3279.63 3280.86 1373 18 0 1 NaN NaN
4 2025-04-25 18:00:00 3277.96 3280.87 3276.92 3280.03 1236 18 0 1 NaN NaN
7 2025-04-25 18:15:00 3272.39 3275.33 3272.39 3273.67 1204 18 0 1 NaN NaN
10 2025-04-25 18:30:00 3272.36 3275.36 3272.02 3274.25 1014 18 0 1 NaN NaN
13 2025-04-25 18:45:00 3280.15 3281.25 3278.42 3280.74 771 18 0 1 NaN NaN
16 2025-04-25 19:00:00 3282.96 3284.49 3281.25 3284.49 839 18 0 1 3285.59 3270.06
19 2025-04-25 19:15:00 3283.87 3284.03 3282.06 3283.46 503 18 0 1 3285.71 3270.06
22 2025-04-25 19:30:00 3283.25 3285.59 3283.12 3284.71 647 18 0 1 3285.71 3270.06
25 2025-04-25 19:45:00 3280.78 3283.15 3280.31 3283.07 696 18 0 1 3285.71 3272.02
28 2025-04-25 20:00:00 3285.31 3285.49 3281.97 3282.23 935 18 0 1 3287.48 3278.42
31 2025-04-25 20:15:00 3287.70 3290.24 3286.50 3289.91 888 18 0 1 3288.42 3280.31
34 2025-04-25 20:30:00 3286.99 3289.14 3286.15 3289.08 754 18 0 1 3291.22 3280.31
37 2025-04-25 20:45:00 3291.99 3292.61 3289.81 3291.86 746 18 0 1 3294.64 3280.31
40 2025-04-25 21:00:00 3293.22 3295.00 3291.48 3293.64 558 18 0 1 3295.10 3280.31
43 2025-04-25 21:15:00 3293.62 3294.76 3292.44 3294.53 424 18 0 1 3297.03 3281.78
46 2025-04-25 21:30:00 3293.21 3294.49 3292.38 3292.54 307 18 0 1 3297.03 3286.15
49 2025-04-25 21:45:00 3291.28 3291.98 3290.34 3290.69 350 18 0 1 3297.03 3286.15
52 2025-04-25 22:00:00 3288.37 3290.12 3288.15 3289.65 522 18 0 1 3297.03 3288.33
55 2025-04-25 22:15:00 3289.72 3292.76 3289.64 3291.62 402 18 0 1 3297.03 3287.78
58 2025-04-25 22:30:00 3294.65 3298.99 3294.65 3298.55 443 18 0 1 3295.21 3287.78
61 2025-04-25 22:45:00 3308.50 3308.56 3304.18 3308.00 699 18 0 1 3308.72 3287.78
64 2025-04-25 23:00:00 3304.77 3306.93 3304.77 3305.68 426 18 0 1 3312.45 3287.78
67 2025-04-25 23:15:00 3305.48 3307.01 3305.44 3306.68 231 18 0 1 3312.45 3287.78
70 2025-04-25 23:30:00 3308.53 3309.31 3308.05 3308.78 172 18 0 1 3312.45 3289.64
73 2025-04-25 23:45:00 3314.35 3315.61 3313.61 3314.07 353 28 0 1 3314.33 3294.65
In [285]:
current_high = lastchart['high'][73]
current_close = lastchart['close'][73]
#sl = lastchart['min_box'][23] - tp_sl_ratio  * (current_high - current_close) # SL at the high of the current candle
#tp = lastchart['max_box'][23] + tp_sl_ratio * (current_high - current_close)
current_diff = lastchart.max_box.max() - lastchart.min_box.min()
lastchart.max_box.max() - lastchart.min_box.min()

#current_close, current_high
Out [285]:
44.26999999999998
In [274]:
last_max = lastchart.max_box.mean()
last_min = lastchart.min_box.mean()
mean_high = round((ath + tdh + ldh + last_max) / 4, 2)
mean_low = round((atl + tdl + ldl + last_min) / 4, 2)
mean_high, mean_low, last_max, last_min
Out [274]:
(3383.78, 3300.72, 3297.017, 3282.3729999999996)

Fibanaccio retracement

The Fibonacci retracement strategy is a popular technical analysis tool to identify potential reversal levels in financial markets and is used by traders. Based on the Fibonacci sequence, this strategy involves plotting key retracement levels. The typical or default levels are 23.6%, 38.2%, 50%, 61.8%, and 78.6%, against a price movement.

  • Total up move = $250 - $200 = $50 38.2% of up move = 38.2% * 50 = $19.1
  • Retracement forecast = $250 - $19.1 = $230.9

e.g.:

res = AllTimeHigh - LastDayLow fb1 = round(AllTimeHigh - (res * 0.236))

The ratios 38.2% and 61.8% are the most important support levels.

Link zur Webseite Fibanaccio

Calculate BuyLimit

Fibanaccio == Take Price

In [275]:
res = ath - ldl
res = ldh - tdl
res = mean_high - tdl
fb1 = round(mean_high - (res * 0.236),2)
fb2 = round(mean_high - (res * 0.382),2)
fb3 = round(mean_high - (res * 0.681),2)
fb4 = round(mean_high - (res * 0.786),2)
fb1, fb2, fb3, fb4
Out [275]:
(3355.76, 3338.42, 3302.92, 3290.45)

Take Profit and Stop Loss

In [276]:
tp = ath - ldh + fb1
sl = fb1 - (tp - ldh)
tp, sl
Out [276]:
(3488.3500000000004, 3234.8199999999997)

Create Dataframe

In [277]:
current_price = mt.symbol_info_tick(symbol)
fb = [fb1, fb2, fb3, fb4]
trading_type = []

x = 1
for i in fb:
    #print(i)
    
    if current_price.bid < i:
        print(f"Aktueller Preis {current_price.bid} ist kleiner als FB{x}  {i}, trading ist BuyStop")
        trading_type.append('BuyStop')
    elif current_price.bid > i: 
        print(f"Aktueller Preis {current_price.bid} ist größer als FB{x} {i}, trading type ist BuyLimit")
        trading_type.append("BuyLimit")
    x += 1

trading_type
Out [277]:
Aktueller Preis 3318.69 ist kleiner als FB1  3355.76, trading ist BuyStop
Aktueller Preis 3318.69 ist kleiner als FB2  3338.42, trading ist BuyStop
Aktueller Preis 3318.69 ist größer als FB3 3302.92, trading type ist BuyLimit
Aktueller Preis 3318.69 ist größer als FB4 3290.45, trading type ist BuyLimit

['BuyStop', 'BuyStop', 'BuyLimit', 'BuyLimit']
In [278]:
ldh - tdl
mean_high - mean_low
Out [278]:
83.0600000000004
In [286]:
tp_factor = ath-ldh
tp_factor = ath - tdh
#tp_factor = ath - mean_low
tp_factor = mean_high - mean_low - current_diff
sl_factor = [tp - fb1, tp - fb2, tp - fb3, tp - fb4]


data = {
    'trade_type' : [trading_type[0] + '01', trading_type[1] + '02', trading_type[2] + '03', trading_type[3] + '04'],
    'trade_price': [fb1, fb2, fb3, fb4],
    'trade_volume': [0.1, 0.2, 0.3, 0.4],
    #'trade_sl': [fb1 - sl_factor, fb2 - sl_factor, fb3 - sl_factor, fb4 - sl_factor],
    #'trade_sl': [fb1 - sl_factor[0], fb2 - sl_factor[1], fb3 - sl_factor[2], fb4 - sl_factor[3]],
    'trade_sl': [fb1 - tp_factor, fb2 - tp_factor, fb3 - tp_factor, fb4 - tp_factor],
    'trade_tp': [tp_factor + fb1, tp_factor + fb2, tp_factor + fb3, tp_factor + fb4]

}
In [280]:
# adj = 15
# factor = 5
# adj2 = adj - 5

# data = {
#     'trade_type' : ['BuyLimit01', 'BuyLimit02', 'BuyLimit03', 'BuyLimit04', 'BuyLimit05', 'BuyLimit06', 'BuyLimit07', 'BuyLimit08', 'BuyLimit09', 'BuyStop01', 'BuyStop02'],
#     'trade_price': [ath - adj, ath - adj - 25, ath - adj - 50, ath - adj - 75, ath - adj - 100, ath - adj - 125, ath - adj - 150, ath - adj - 175, ath - adj - 200, ath - 66, ath - adj],
#     'trade_volume': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.1, 0.1],
#     'trade_sl': [ath - adj - factor, ath - adj - 25 - factor, ath - adj - 50 - factor, ath - adj - 75 - factor, ath - adj - 100 - factor, ath - adj - 125 - factor, ath - adj - 150 - factor, ath - adj - 175 - factor, ath - adj - 200 - factor, ath - 66 - 20, ath - adj - 20],
#     'trade_tp': [ath - adj2, ath - adj2, ath - adj2, ath - adj2, ath - adj2, ath - adj2, ath - adj2, ath - adj2, ath - adj2, ath, ath]

# }
In [288]:
df = pd.DataFrame(data)
df
Out [288]:
trade_type trade_price trade_volume trade_sl trade_tp
0 BuyStop01 3355.76 0.1 3316.97 3394.55
1 BuyStop02 3338.42 0.2 3299.63 3377.21
2 BuyLimit03 3302.92 0.3 3264.13 3341.71
3 BuyLimit04 3290.45 0.4 3251.66 3329.24

Set volume on BuyStops to 0.1

In [282]:
for index, row in df.iterrows():
    if bool(re.search('^BuyStop[0-9]{2}', row.trade_type)):
        df.loc[index, ['trade_volume']] = 0.1
    print(row.trade_type, row.trade_volume, index)
    
BuyStop01 0.1 0
BuyStop02 0.2 1
BuyLimit03 0.3 2
BuyLimit04 0.4 3
In [283]:
df
Out [283]:
trade_type trade_price trade_volume trade_sl trade_tp
0 BuyStop01 3355.76 0.1 3272.70 3438.82
1 BuyStop02 3338.42 0.1 3255.36 3421.48
2 BuyLimit03 3302.92 0.3 3219.86 3385.98
3 BuyLimit04 3290.45 0.4 3207.39 3373.51
In [284]:
#if bool(re.search('^BuyLimit[0-9]{2}', df.trade_type[1])) and df.trade_volume[0] == 0.1 and df.trade_volume[1] != 0.1:
#    df.at[2, 'trade_volume'] = df.trade_volume[1] + 0.1

#if df.trade_volume[2] != 0.1 and df.trade_volume[2] != 0.1:
#    df.at[2, 'trade_volume'] = df.trade_volume[1] + 0.1

#df

Place Order

Pending Orders in MT5

Pending orders allow you to execute trades at your predetermined levels rather than at the current market price. There are four types of pending orders:

  • Buy Limit: It is a pending order to buy an asset below the current market price. It is ideal for buying at a support level during correction.

  • Sell Limit: It is an order to sell an asset above the current market price. It helps to short-sell at the resistance level.

  • Buy Stop: It is a buy order used to catch the bullish breakouts. You can place a stop above the current market price.

  • Sell Stop: It is a sell order used to catch a bearish breakout by putting an order below the current market price.

In [168]:
if bool(re.search('^StopLimit[0-9]{2}', 'BuyLimit01')):
    print('Wahr')
else:
    print('Falsch')
Falsch
In [169]:
def buyOrder(trade_type: str, trade_price: float, trade_volume: float, trade_sl: float, trade_tp: float, symbol: str):
    
    if bool(re.search('^BuyLimit[0-9]{2}', trade_type)):
        # Buy Stop
        request = {
        "action": mt.TRADE_ACTION_PENDING,
        "symbol": symbol,
        "volume": trade_volume, # FLOAT
        "type": mt.ORDER_TYPE_BUY_LIMIT,
        "price": trade_price,
        "sl": trade_sl, # FLOAT
        "tp": trade_tp, # FLOAT
        "deviation": 20, # INTERGER
        "magic": 0, # INTERGER
        "comment": trade_type,
        "type_time": mt.ORDER_TIME_GTC,
        "type_filling": mt.ORDER_FILLING_IOC,
        }
    else:
       #Sell Stop 
        request = {
        "action": mt.TRADE_ACTION_PENDING,
        "symbol": symbol,
        "volume": trade_volume, # FLOAT
        "type": mt.ORDER_TYPE_BUY_STOP,
        "price": trade_price,
        "sl": trade_sl, # FLOAT
        "tp": trade_tp, # FLOAT
        "deviation": 20, # INTERGER
        "magic": 0, # INTERGER
        "comment": trade_type,
        "type_time": mt.ORDER_TIME_GTC,
        "type_filling": mt.ORDER_FILLING_IOC,
        }

    order = mt.order_send(request)

Place Orders

In [292]:
for index, row in df.iterrows():
    #print(index, row)
    print(row['trade_type'], row['trade_price'], row['trade_volume'], row['trade_sl'], row['trade_tp'], symbol)
    buyOrder(row['trade_type'], row['trade_price'], row['trade_volume'], row['trade_sl'], row['trade_tp'], symbol)

    
BuyStop01 3355.76 0.1 3316.97 3394.5500000000006 XAUUSD
BuyStop02 3338.42 0.2 3299.6299999999997 3377.2100000000005 XAUUSD
BuyLimit03 3302.92 0.3 3264.1299999999997 3341.7100000000005 XAUUSD
BuyLimit04 3290.45 0.4 3251.6599999999994 3329.2400000000002 XAUUSD

Remove Pending Orders

In [289]:
# Remove Pending Order

def rm_pending_orders():

    while mt.orders_total() > 0:
        pending_orders = mt.orders_get()
        order1 = pending_orders[0]
        request = {
        'action': mt.TRADE_ACTION_REMOVE,
        'order': order1.ticket
        }
        mt.order_send(request)
In [291]:
rm_pending_orders()
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
Cell In[291], line 1
----> 1 rm_pending_orders()

Cell In[289], line 5, in rm_pending_orders()
      3 def rm_pending_orders():
----> 5     while mt.orders_total() > 0:
      6         pending_orders = mt.orders_get()
      7         order1 = pending_orders[0]

KeyboardInterrupt:

Check pending Orders

In [ ]:
pos = mt.orders_get()
for i in pos:
    print(i.comment)
    df.loc[df['trade_type'] == i.comment, 'pending_trades'] = 1
df['pending_trades'] = df['pending_trades']. fillna(0)
In [ ]:
df.loc[df['trade_type'].isin(['BuyLimit02', 'BuyLimit03'])]
In [ ]:
row = df.loc[df['trade_type'] == 'BuyLimit02']
df.loc[df['trade_type'] == 'BuyLimit02', 'pending_trades'] = 1
In [ ]:
df
In [ ]:
tp_price = 30

pos = mt.positions_get()
pos[0].profit
pos[0].price_open + tp_price
3366.26

Check for open trades

In [48]:
pos = mt.positions_get()
for i in pos:
    print(i)
    pos_comment = i.comment
    pos_profit = i.profit
    print(pos_comment, pos_profit)
    df.loc[df['trade_type'] == i.comment, 'open_trades'] = 1
df['open_trades'] = df['open_trades']. fillna(0)
TradePosition(ticket=238422628, time=1745410731, time_msc=1745410731060, time_update=1745410731, time_update_msc=1745410731060, type=0, magic=0, identifier=238422628, reason=3, volume=0.1, price_open=3336.26, sl=3190.17, tp=3482.35, price_current=3303.56, swap=-18.48, profit=-327.0, symbol='XAUUSD', comment='BuyStop04', external_id='')
BuyStop04 -327.0
TradePosition(ticket=239454126, time=1745465427, time_msc=1745465427533, time_update=1745465427, time_update_msc=1745465427533, type=0, magic=0, identifier=239454126, reason=3, volume=0.1, price_open=3336.7, sl=3169.39, tp=3504.01, price_current=3303.56, swap=-4.62, profit=-331.4, symbol='XAUUSD', comment='BuyStop03', external_id='')
BuyStop03 -331.4
TradePosition(ticket=239454136, time=1745456864, time_msc=1745456864432, time_update=1745456864, time_update_msc=1745456864432, type=0, magic=0, identifier=239454136, reason=3, volume=0.1, price_open=3311.53, sl=3144.22, tp=3478.84, price_current=3303.56, swap=-4.62, profit=-79.7, symbol='XAUUSD', comment='BuyStop04', external_id='')
BuyStop04 -79.7
In [49]:
df
Out [49]:
trade_type trade_price trade_volume trade_sl trade_tp open_trades
0 BuyStop01 3450.07 0.1 3331.67 3568.47 0.0
1 BuyStop02 3419.19 0.1 3300.79 3537.59 0.0
2 BuyStop03 3355.93 0.1 3237.53 3474.33 1.0
3 BuyLimit04 3333.72 0.4 3215.32 3452.12 0.0
In [ ]:
open_trades = df.loc[df['open_trades'] == 1]
open_trades
In [ ]:
for index, row in open_trades.iterrows():
    print(row.trade_type)
In [ ]:
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