This may be a bit overkill for what you are looking for but with parts of the code you can find the spot price history for the last hour (this can be changed). It'll give you the instance type, AZ, and additional information. From there you can loop through the instance type to by AZ. If a spot instance doesn't come up in say 30 seconds try the next AZ.

And to Ahmed's point in his answer, this information can be used in the spot_fleet_request instead of looping through the AZs. If you pass the wrong AZ or subnet in the spot fleet request, it may pass the dryrun api call, but can still fail the real call. Just a heads up on that if you are using the dryrun parameter.

Here's the output of the code that follows:

In [740]: df_spot_instance_options
Out[740]:
    AvailabilityZone   InstanceType  SpotPrice  MemSize  vCPUs  CurrentGeneration Processor
0         us-east-1d        t3.nano      0.002      512      2               True  [x86_64]
1         us-east-1b        t3.nano      0.002      512      2               True  [x86_64]
2         us-east-1a        t3.nano      0.002      512      2               True  [x86_64]
3         us-east-1c        t3.nano      0.002      512      2               True  [x86_64]
4         us-east-1d       t3a.nano      0.002      512      2               True  [x86_64]
..               ...            ...        ...      ...    ...                ...       ...
995       us-east-1a    p2.16xlarge      4.320   749568     64               True  [x86_64]
996       us-east-1b    p2.16xlarge      4.320   749568     64               True  [x86_64]
997       us-east-1c    p2.16xlarge      4.320   749568     64               True  [x86_64]
998       us-east-1d    p2.16xlarge     14.400   749568     64               True  [x86_64]
999       us-east-1c  p3dn.24xlarge      9.540   786432     96               True  [x86_64]

[1000 rows x 7 columns]

And here's the code:

import boto3
import pandas as pd
from datetime import datetime, timedelta

ec2c = boto3.client('ec2')
ec2r = boto3.resource('ec2')

#### The rest of this code maps the instance details to spot price in case you are looking for certain memory or cpu
paginator = ec2c.get_paginator('describe_instance_types')
response_iterator = paginator.paginate( )

df_hold_list = []
for page in response_iterator:
    df_hold_list.append(pd.DataFrame(page['InstanceTypes']))

df_instance_specs = pd.concat(df_hold_list, axis=0).reset_index(drop=True)
df_instance_specs['Spot'] = df_instance_specs['SupportedUsageClasses'].apply(lambda x: 1 if 'spot' in x else 0)
df_instance_spot_specs = df_instance_specs.loc[df_instance_specs['Spot']==1].reset_index(drop=True)

#unapck memory and cpu dictionaries
df_instance_spot_specs['MemSize'] = df_instance_spot_specs['MemoryInfo'].apply(lambda x: x.get('SizeInMiB'))
df_instance_spot_specs['vCPUs'] = df_instance_spot_specs['VCpuInfo'].apply(lambda x: x.get('DefaultVCpus'))
df_instance_spot_specs['Processor'] = df_instance_spot_specs['ProcessorInfo'].apply(lambda x: x.get('SupportedArchitectures'))

#look at instances only between 30MB and 70MB
instance_list = df_instance_spot_specs['InstanceType'].unique().tolist()

#---------------------------------------------------------------------------------------------------------------------
# You can use this section by itself to get the instancce type and availability zone and loop through the instance you want
# just modify instance_list with one instance you want informatin for
#look only in us-east-1
client = boto3.client('ec2', region_name='us-east-1')
prices = client.describe_spot_price_history(
    InstanceTypes=instance_list,
    ProductDescriptions=['Linux/UNIX', 'Linux/UNIX (Amazon VPC)'],
    StartTime=(datetime.now() -
               timedelta(hours=1)).isoformat(),
               # AvailabilityZone='us-east-1a'
    MaxResults=1000)

df_spot_prices = pd.DataFrame(prices['SpotPriceHistory'])
df_spot_prices['SpotPrice'] = df_spot_prices['SpotPrice'].astype('float')
df_spot_prices.sort_values('SpotPrice', inplace=True)
#---------------------------------------------------------------------------------------------------------------------

# merge memory size and cpu information into this dataframe
df_spot_instance_options = df_spot_prices[['AvailabilityZone', 'InstanceType', 'SpotPrice']].merge(df_instance_spot_specs[['InstanceType', 'MemSize', 'vCPUs',
                                            'CurrentGeneration', 'Processor']], left_on='InstanceType', right_on='InstanceType')

Yes, you can get the pricing history and create some charts or maybe machine learning analysis.

You can use AWS CLI and its aws ec2 describe-spot-price-history command. It accepts a lot of parameters to narrow down the price history you want to a specific region, availability zone, instance type, etc:

~ $ aws ec2 describe-spot-price-history
      [--filters <value>]
      [--instance-types <value>]
      [--availability-zone <value>]
      [--start-time <value>]
      [--end-time <value>]
      [--product-descriptions <value>]
      ...

Be aware that by default it returns a lot of data - it may look like it stalls so give it some time or narrow down the scope with parameters.

Also depending on your aws-cli configuration it may return JSON by default. If you prefer tab separated values use aws --output=text ....

Hope that helps :)

would be nice to have this, but afaik there is no such thign